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Volume 8
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J. Manuf. Mater. Process., Volume 8, Issue 3 (June 2024) – 43 articles

Cover Story (view full-size image): The present study shows the results of high-speed orthogonal cutting tests conducted on rectangular and circular specimens of unidirectional glass fiber reinforced plastics (GFRPs). The aims of this study were to examine chip morphology and to correlate it with cutting force trends, as well as to evaluate the machined surface quality, which is heavily influenced by material anisotropy and other cutting parameters. We this approach, we aimed to gain precise insights into the phenomenon while simultaneously optimizing all aspects of the cutting process of composite materials. At the same time, this study aimed to provide a simple experimental methodology to obtain results that can also be applied to more complex machining operations for composite materials. View this paper
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22 pages, 14393 KiB  
Article
Pressure and Liquid Distribution under the Blade of a Basket Extruder of Continuous Wet Granulation of Model Material
by Roman Fekete, Peter Peciar, Martin Juriga, Štefan Gužela, Michaela Peciarová, Dušan Horváth and Marian Peciar
J. Manuf. Mater. Process. 2024, 8(3), 127; https://doi.org/10.3390/jmmp8030127 - 18 Jun 2024
Viewed by 808
Abstract
This study explores the influence of blade design on the low-pressure extrusion process, which is relevant to techniques like spheronization. We investigate how blade geometry affects the extruded paste and final product properties. A model paste was extruded through a basket extruder with [...] Read more.
This study explores the influence of blade design on the low-pressure extrusion process, which is relevant to techniques like spheronization. We investigate how blade geometry affects the extruded paste and final product properties. A model paste was extruded through a basket extruder with varying blade lengths to create distinct wedge gaps (20°, 26° and 32° contact angles). The theoretical analysis explored paste behavior within the gap and extrudate. A model material enabled objective comparison across blade shapes. Our findings reveal a significant impact of blade design on the pressure profile, directly influencing liquid distribution in the paste and extrudate. It also affects the required torque relative to extruder output. The findings of this study hold significant implications for continuous granulation, a technique employed in the pharmaceutical industry for producing granules with uniform size and properties. Understanding the influence of blade geometry on the extrusion process can lead to the development of optimized blade designs that enhance granulation efficiency, improve product quality, and reduce energy consumption. By tailoring blade geometry, manufacturers can achieve more consistent granule characteristics, minimize process variability, and ultimately produce pharmaceuticals with enhanced efficacy. Full article
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Figure 1
<p>Configuration of the paste skeleton of different materials: (1) sand; (2) very finely ground limestone; (3) a mixture of sand and very finely ground limestone. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>&gt;</mo> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>&lt;</mo> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>∆</mo> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>&lt;</mo> <mo>∆</mo> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>&gt;</mo> <mo>∆</mo> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 2
<p>Radial extruder with the different geometries of the extrusion blade: (1) blade; (2) matrix; (3) paste; (4) extrudate.</p> Full article ">Figure 3
<p>Scheme of influences of extrusion parameters.</p> Full article ">Figure 4
<p>Laboratory radial extruder. Main parameters of the extruder: (1) matrix; (2) base plate; (3) bearing; (4) frame; (5) arm; (6) force sensor; (7) blade; (8) rotor with shaft; (9) lid; (10) paste; and (11) pressure sensor.</p> Full article ">Figure 5
<p>The main experimental steps and the definition of control volumes and pressure profile under the blade as a function of the blade position: (<b>a</b>) the extruder chamber filled with colored layers of paste before the experiment; (<b>b</b>) colored streams of the paste in the wedge after the experiment; (<b>c</b>) the wedge of paste from the chamber before cutting; (<b>d</b>) extrudate behind the matrix; (<b>e</b>) definition of control volumes.</p> Full article ">Figure 6
<p>The pressure profiles in the wedge during extrusion: (<b>a</b>) before the conversion as the graphical record of the sensor; (<b>b</b>) after the time interval <math display="inline"><semantics> <mrow> <mo>∆</mo> <mi>t</mi> </mrow> </semantics></math> conversion to the projection length of the blade <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mi>M</mi> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 7
<p>The process of the paste extrusion. The distribution of the extrusion pressure under the blade and the diagram of the principle of extrudate formation depending on the position of the blade, the weight of the extrudate <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>m</mi> </mrow> <mrow> <mi>e</mi> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math> and the mass flow <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>m</mi> </mrow> <mrow> <mi>t</mi> <mi>e</mi> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 8
<p>Comparing the liquid distribution: (<b>a</b>) in the wedge gap between the blade and the matrix; (<b>b</b>) in the extrudate; (<b>c</b>) interval of scattering of the average values of the moisture in the wedge for all speeds of the rotor; (<b>d</b>) interval of scattering of the average values of the moisture in the extrudate for all speeds of the rotor.</p> Full article ">Figure 9
<p>Balance of extruded paste in the control volumes outside the matrix: (<b>a</b>) weight in individual control volumes <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>e</mi> <mi>x</mi> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math>; (<b>b</b>) weight of paste extruded through the die holes in control volumes <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>e</mi> <mi>x</mi> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math> per unit time.</p> Full article ">Figure 9 Cont.
<p>Balance of extruded paste in the control volumes outside the matrix: (<b>a</b>) weight in individual control volumes <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>e</mi> <mi>x</mi> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math>; (<b>b</b>) weight of paste extruded through the die holes in control volumes <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>e</mi> <mi>x</mi> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math> per unit time.</p> Full article ">Figure 10
<p>Influence of the extrusion pressure <math display="inline"><semantics> <mrow> <mi>P</mi> </mrow> </semantics></math>, blade geometry and rotor speed <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>n</mi> </mrow> <mrow> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math>: (<b>a</b>) on the distribution of the liquid before the matrix <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>w</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math>, in the extrudate <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>w</mi> <mi>e</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math>; (<b>b</b>) on the mass flow of the extrudate through the openings in the matrix <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>m</mi> </mrow> <mrow> <mi>t</mi> <mi>e</mi> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 11
<p>Influence of the mass flow of the extrudate through the holes in the matrix <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>m</mi> </mrow> <mrow> <mi>t</mi> <mi>e</mi> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math> on the torque <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>M</mi> </mrow> <mrow> <mi>k</mi> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math> required to drive the rotor with the blade.</p> Full article ">
25 pages, 16854 KiB  
Article
Optimizing the Electrical Discharge Machining Process Parameters of the Nimonic C263 Superalloy: A Sustainable Approach
by Renu Kiran Shastri, Chinmaya Prasad Mohanty, Umakant Mishra, Tapano Kumar Hotta, Viraj Vishwas Patil and Konda Gokuldoss Prashanth
J. Manuf. Mater. Process. 2024, 8(3), 126; https://doi.org/10.3390/jmmp8030126 - 15 Jun 2024
Viewed by 1144
Abstract
Engineers continue to be concerned about electrical discharge-machined components’ high energy consumption, machining debris, and poor dimensional precision. The aim of this research is to propose a hybrid neuro-genetic approach to improve the machinability of the electrical discharge machining (EDM) of the Nimonic [...] Read more.
Engineers continue to be concerned about electrical discharge-machined components’ high energy consumption, machining debris, and poor dimensional precision. The aim of this research is to propose a hybrid neuro-genetic approach to improve the machinability of the electrical discharge machining (EDM) of the Nimonic C263 superalloy. This approach focuses on reducing the energy consumption and negative environmental impacts. The material removal rate (MRR), electrode wear ratio (EWR), specific energy consumption (SEC), surface roughness (Ra), machining debris (db), and circularity (C) are examined as a function of machining parameters such as the voltage (V), pulse on time (Ton), current (I), duty factor (τ), and electrode type. By employing the VIKOR method, all the responses are transformed into a distinctive VIKOR index (VI). Neuro-genetic methods (a hybrid VIKOR-based ANN-GA) can further enhance the best possible result from the VIKOR index. During this step, the hybrid technique (VIKOR-based ANN-GA) is used to estimate an overall improvement of 9.87% in the response, and an experiment is conducted to confirm this condition of optimal machining. This work is competent enough to provide aeroengineers with an energy-efficient, satisfying workplace by lowering the machining costs and increasing productivity. Full article
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<p>The methodology employed in the current work.</p> Full article ">Figure 2
<p>Mitutoyo Surftest SJ-210.</p> Full article ">Figure 3
<p>(<b>a</b>) Centrifugal machining setup, (<b>b</b>) debris collected during machining, and (<b>c</b>) FESEM setup.</p> Full article ">Figure 4
<p>Energy meter setup.</p> Full article ">Figure 5
<p>Main effect graphs for the <span class="html-italic">MRR</span> (<b>a</b>) Effect of Current on <span class="html-italic">MRR</span> (<b>b</b>) Effect of Electrode on <span class="html-italic">MRR</span> (<b>c</b>) Effect of Pulse on time on <span class="html-italic">MRR</span>.</p> Full article ">Figure 6
<p>Main effect graphs for the <span class="html-italic">EWR</span> (<b>a</b>) Effect of Current on <span class="html-italic">EWR</span> (<b>b</b>) Effect of Electrode on <span class="html-italic">EWR</span>.</p> Full article ">Figure 7
<p>Main effect graphs for the SR (<b>a</b>) Effect of Current on SR (<b>b</b>) Effect of Electrode on SR.</p> Full article ">Figure 8
<p>The circularity of the machined holes produced by (<b>a</b>) tungsten, (<b>b</b>) copper–tungsten, and (<b>c</b>) copper.</p> Full article ">Figure 9
<p>Main effect graphs for the circularity (<b>a</b>) Effect of Current on circularity (<b>b</b>) Effect of Electrode on circularity.</p> Full article ">Figure 10
<p>Main effect graphs for the debris weight (<b>a</b>) Effect of Current on Debris weight (<b>b</b>) Effect of Electrode on debris weight.</p> Full article ">Figure 11
<p>FESEM of debris machined with W electrode at (<b>a</b>) 5 A, (<b>b</b>) 7 A, and (<b>c</b>) 9 A current.</p> Full article ">Figure 11 Cont.
<p>FESEM of debris machined with W electrode at (<b>a</b>) 5 A, (<b>b</b>) 7 A, and (<b>c</b>) 9 A current.</p> Full article ">Figure 12
<p>FESEM of debris machined with Cu-W electrode at (<b>a</b>) 5 A, (<b>b</b>) 7 A, and (<b>c</b>) 9 A current.</p> Full article ">Figure 13
<p>FESEM of debris machined with Cu electrode at (<b>a</b>) 5 A, (<b>b</b>) 7 A, and (<b>c</b>) 9 A current.</p> Full article ">Figure 14
<p>EDAX analysis of debris produced by (<b>a</b>) W, (<b>b</b>) Cu-W, and (<b>c</b>) Cu electrode.</p> Full article ">Figure 15
<p>Main effect graphs of the <span class="html-italic">SEC</span> (<b>a</b>) Effect of Pulse on time on <span class="html-italic">SEC</span> (<b>b</b>) Effect of Electrode on <span class="html-italic">SEC</span>.</p> Full article ">Figure 16
<p><span class="html-italic">VI</span> fitness values for different generations.</p> Full article ">
15 pages, 5556 KiB  
Article
The Machining and Surface Modification of H13 Die Steel via the Electrical Discharge Machining Process Using Graphite Mixed Dielectric
by Dharmesh Singh, Parveen Goyal and Shankar Sehgal
J. Manuf. Mater. Process. 2024, 8(3), 125; https://doi.org/10.3390/jmmp8030125 - 14 Jun 2024
Viewed by 915
Abstract
Surface modification through electrical discharge machining (EDM) results in many advantages, such as improved surface hardness, enhanced wear resistance, and better micro-structuring. During EDM-based surface modification, either the eroding tool electrode or a powder-mixed dielectric can be utilized to add material onto the [...] Read more.
Surface modification through electrical discharge machining (EDM) results in many advantages, such as improved surface hardness, enhanced wear resistance, and better micro-structuring. During EDM-based surface modification, either the eroding tool electrode or a powder-mixed dielectric can be utilized to add material onto the machined surface of the workpiece. The current study looks at the surface modification of H13 die steel using EDM in a dielectric medium mixed with graphite powder. The experiments were carried out using a Taguchi experimental design. In this work, peak current, pulse-on time, and powder concentration are taken into consideration as input factors. Tool wear rate (TWR), material removal rate (MRR), and the microhardness of the surface of the machined specimen are taken as output parameters. The machined surface’s microhardness was found to have improved by 159%. The results of X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy (EDS) analysis and changes in MRR and TWR due to the powder-mixed dielectric are also discussed in detail. Full article
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<p>The setup of powder-mixed electric discharge machining.</p> Full article ">Figure 2
<p>Electrical discharge machine used in the research.</p> Full article ">Figure 3
<p>MRR in EDM without powder.</p> Full article ">Figure 4
<p>TWR in EDM without powder.</p> Full article ">Figure 5
<p>Microhardness in EDM without powder.</p> Full article ">Figure 6
<p>MRR of powder-mixed EDM with powders of different concentrations.</p> Full article ">Figure 7
<p>TWR of powder-mixed EDM with powders of different concentrations.</p> Full article ">Figure 8
<p>XRD graph of specimen.</p> Full article ">Figure 8 Cont.
<p>XRD graph of specimen.</p> Full article ">Figure 9
<p>(<b>a</b>) Hardness test image of specimen before machining. (<b>b</b>) Hardness test image of machined specimen.</p> Full article ">Figure 10
<p>Microhardness of powder-mixed EDM with powders of different concentrations.</p> Full article ">Figure 11
<p>MRR vs. peak current of experiments conducted with and without powder.</p> Full article ">Figure 12
<p>TWR vs. peak current of experiments conducted with and without powder.</p> Full article ">Figure 13
<p>Microhardness and peak current of experiments conducted with and without powder.</p> Full article ">
19 pages, 7997 KiB  
Article
Improving Deposited Surface Quality in Additive Manufacturing Using Structured Light Scanning Characterization and Mechanistic Modeling
by Tuhin Mukherjee, Weijun Shen, Yiliang Liao and Beiwen Li
J. Manuf. Mater. Process. 2024, 8(3), 124; https://doi.org/10.3390/jmmp8030124 - 14 Jun 2024
Viewed by 1106
Abstract
The surface quality of parts fabricated using laser-directed energy deposition additive manufacturing significantly affects the fatigue life, corrosion resistance, and performance of the components. Surface quality improvements remain a key challenge in laser-directed energy deposition because of the involvement of multiple simultaneously occurring [...] Read more.
The surface quality of parts fabricated using laser-directed energy deposition additive manufacturing significantly affects the fatigue life, corrosion resistance, and performance of the components. Surface quality improvements remain a key challenge in laser-directed energy deposition because of the involvement of multiple simultaneously occurring physical phenomena controlling the surface characteristics. Here, a unique combination of structured light scanning characterization and mechanistic modeling was used to identify three key physical factors that affect surface quality. These factors include a geometric factor, an instability factor, and a disintegration factor, which were calculated using a mechanistic model and correlated with the surface characteristics data obtained from the structured light scanning characterization. It was found that these factors can precisely explain the variations in the average surface roughness. In addition, skewness and kurtosis of the surfaces made by laser-directed energy deposition were found to be significantly better than those observed in traditional manufacturing. Based on the experimental and modeling results, a surface quality process map was constructed that can guide engineers in selecting appropriate sets of process variables to improve deposit surface quality in additive manufacturing. Full article
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Figure 1
<p>Schematic representation of the methodology used in this work. Structured light scanning was used to measure the surface profile of the parts made by laser-directed energy deposition (LDED). A mechanistic model of LDED process was used to calculate the three physical factors responsible for determining the surface qualities of parts. The computed values of the physical factors were tested against the surface characteristics measured by the structure light scanning.</p> Full article ">Figure 2
<p>(<b>a</b>) Actual setup and (<b>b</b>) schematic representation of the structured light scanning method for measuring the surface characteristics.</p> Full article ">Figure 3
<p>Image of the (<b>a</b>) 3D isometric view and (<b>b</b>) top surface of a stainless steel 316 part fabricated by laser-directed energy deposition. The part is 22 mm long and 22 mm wide and is made using 500 W laser power, 9.17 mm/s scanning speed, and 0.083 g/s powder mass flow rate. (<b>c</b>) Surface profile of the part within the region shown by a black dashed box in figure (<b>b</b>). The color bar represents the surface height or depth in mm.</p> Full article ">Figure 4
<p>Temperature profile on the longitudinal section (the plane along the scanning direction) of the deposit computed using (<b>a</b>) a mechanistic model used in this work and (<b>b</b>) a heat transfer fluid flow model [<a href="#B39-jmmp-08-00124" class="html-bibr">39</a>]. The result in Figure (<b>b</b>) was provided in [<a href="#B39-jmmp-08-00124" class="html-bibr">39</a>]. Both the results are for stainless steel 316 deposit made by laser-directed energy deposition using 2500 W laser power, 10.6 mm/s scanning speed, and 0.25 g/s powder mass flow rate.</p> Full article ">Figure 5
<p>Temperature profiles on the longitudinal section (the plane along the scanning direction) of stainless steel 316 deposits computed using a mechanistic model used in this work. The results are for 0.083 g/s powder mass flow rate and (<b>a</b>) 600 W and 12.5 mm/s scanning speed, (<b>b</b>) 700 W and 12.5 mm/s scanning speed, and (<b>c</b>) 600 W and 10.83 mm/s scanning speed.</p> Full article ">Figure 6
<p>Comparison between the deposit cross-sections computed using the model used in this work and the experimentally measured cross-sections [<a href="#B39-jmmp-08-00124" class="html-bibr">39</a>] of stainless steel 316 deposits made by laser-directed energy deposition using (<b>a</b>) 1500 W and (<b>b</b>) 2500 W laser power. For both cases, scanning speed and powder mass flow rate were 10.6 mm/s and 0.25 g/s, respectively. Yellow dashed lines indicate the fusion zone boundaries.</p> Full article ">Figure 7
<p>The ratio of the average surface roughness of the parts to powder diameter is represented as functions of (<b>a</b>) the dimensionless geometry factor, (<b>b</b>) Richardson number indicating the instability factor, and (<b>c</b>) the dimensionless disintegration factor. Here, the average surface roughness values are measured using the structured light scanning. Corresponding values of the three physical factors are calculated using the model used in this work. The error bars in the data points indicate the measurement error in the surface roughness values. Mean absolute errors (MAE) for (<b>a</b>–<b>c</b>) are 0.015, 0.059, and 0.091, respectively. In addition, Root Mean Square Errors (RMSEs) for (<b>a</b>–<b>c</b>) are 0.021, 0.079, and 0.114, respectively. The data are for parts made using 0.083 g/s powder mass flow rate. Laser power was varied between 400 and 700 W with a step of 100 W and the scanning speed was varied between 450 and 750 mm/min with a step of 50 mm/min.</p> Full article ">Figure 8
<p>The effects of the dimensionless surface quality number on (<b>a</b>) the ratio of the average surface roughness of the parts to powder diameter, (<b>b</b>) surface profile skewness, and (<b>c</b>) surface profile kurtosis. Here, the average surface roughness values, surface skewness, and kurtosis are measured using structured light scanning. Corresponding values of the dimensionless surface quality number are calculated using the model used in this work. The dimensionless surface quality number is represented by the product of the three dimensionless physical factors. The data are for parts made using 0.083 g/s powder mass flow rate. Laser power was varied between 400 and 700 W with a step of 100 W and the scanning speed was varied between 450 and 750 mm/min with a step of 50 mm/min.</p> Full article ">Figure 9
<p>Comparison of the surface profile skewness and kurtosis of parts made by laser-directed energy deposition in this work with those fabricated using traditional manufacturing processes such as milling, honing, grinding, electro-discharge machining (EDM), and turning. The data on the traditional manufacturing processes are taken from [<a href="#B45-jmmp-08-00124" class="html-bibr">45</a>].</p> Full article ">Figure 10
<p>(<b>a</b>) A surface quality map indicating the variations in the computed dimensionless surface quality number with heat input (laser power/scanning speed) and powder mass flow rate. The dimensionless surface quality number is represented by the product of the three dimensionless physical factors. (<b>b</b>,<b>c</b>) Images of the top surface of two stainless steel 316 parts fabricated by laser-directed energy deposition using corresponding conditions shown in (<b>a</b>).</p> Full article ">
14 pages, 8100 KiB  
Article
Additive Manufacturing of Ti3AlC2/TiC and Ti3AlC2/SiC Ceramics Using the Fused Granules Fabrication Technique
by Maksim Krinitcyn, Georgy Kopytov and Egor Ryumin
J. Manuf. Mater. Process. 2024, 8(3), 123; https://doi.org/10.3390/jmmp8030123 - 13 Jun 2024
Viewed by 1030
Abstract
In this work, SiC–Ti3AlC2 and TiC–Ti3AlC2 composites produced by additive manufacturing are investigated. The issue of obtaining ceramic materials using additive manufacturing technologies is currently relevant, since not many modern additive technologies are suitable for working with [...] Read more.
In this work, SiC–Ti3AlC2 and TiC–Ti3AlC2 composites produced by additive manufacturing are investigated. The issue of obtaining ceramic materials using additive manufacturing technologies is currently relevant, since not many modern additive technologies are suitable for working with ceramic materials. The study is devoted to the optimization of additive manufacturing parameters, as well as the study of the structure and properties of the resulting objects. The fused granules fabrication (FGF) method as one kind of the material extrusion additive manufacturing (MEAM) technology is used to obtain composite samples. The main advantage of the FGF technology is the ability to obtain high-quality samples from ceramic materials by additive manufacturing. Composites with different ratios between components and different powder/polymer ratios are investigated. The technological features of the additive formation of composites are investigated, as well as their structure and properties. The optimal sintering temperature to form the best mechanical properties for both composites is 1300 °C. The composites have a regulatable porosity. Ti3AlC2 content, sintering temperature, and polymer content in the feedstock are the main parameters that regulate the porosity of FGF samples. Full article
(This article belongs to the Special Issue Advances in Material Forming: 2nd Edition)
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<p>Slicer calibration model (<b>a</b>) and cross-sections of samples obtained with extrusion coefficients (<b>b</b>) 1.04, (<b>c</b>) 1.30, and (<b>d</b>) 1.43.</p> Full article ">Figure 2
<p>Flow calibration model.</p> Full article ">Figure 3
<p>Green sample obtained by FGF technology.</p> Full article ">Figure 4
<p>Polymer reduction during debinding (<b>a</b>) and mass change (<b>b</b>) at different temperatures for samples with TiC:TAC = 70:30, powder/polymer = 70:30.</p> Full article ">Figure 5
<p>Minimum debinding time required to bring the binder content to less than 1%.</p> Full article ">Figure 6
<p>Fracture surface (<b>a</b>,<b>c</b>) and cross-section (<b>b</b>) of the FGF (<b>a</b>,<b>b</b>) and SPS (<b>c</b>) samples of TiC:TAC = 70:30.</p> Full article ">Figure 7
<p>Bending strength of samples with different TiC:TAC and powder/polymer ratio, sintered at 1250 °C (<b>a</b>) and 1300 °C (<b>b</b>).</p> Full article ">Figure 8
<p>Fracture surface (<b>a</b>,<b>c</b>) and cross-section (<b>b</b>) of the FGF (<b>a</b>,<b>b</b>) and SPS (<b>c</b>) samples of SiC:TAC = 70:30.</p> Full article ">Figure 9
<p>XRD spectra of SiC:TAC samples sintered at 1300 °C with different SiC:TAC ratios.</p> Full article ">Figure 10
<p>Bending strength and open porosity of the SiC:TAC samples sintered at different temperatures.</p> Full article ">Figure 11
<p>Strength–porosity relationship of SiC:TAC samples sintered at different temperatures.</p> Full article ">
16 pages, 1316 KiB  
Article
Stability of Micro-Milling Tool Considering Tool Breakage
by Yuan-Yuan Ren, Bao-Guo Jia, Min Wan and Hui Tian
J. Manuf. Mater. Process. 2024, 8(3), 122; https://doi.org/10.3390/jmmp8030122 - 11 Jun 2024
Viewed by 1130
Abstract
Micro-milling, widely employed across various fields, faces significant challenges due to the small diameter and limited stiffness of its tools, making the process highly susceptible to cutting chatter and premature tool breakage. Ensuring stable and safe cutting processes necessitates the prediction of chatter [...] Read more.
Micro-milling, widely employed across various fields, faces significant challenges due to the small diameter and limited stiffness of its tools, making the process highly susceptible to cutting chatter and premature tool breakage. Ensuring stable and safe cutting processes necessitates the prediction of chatter by considering the tool breakage. Crucially, the modal parameters of the spindle–holder–tool system are important prerequisites for such stability prediction. In this paper, the frequency response functions (FRFs) of the micro-milling tool are calculated by direct FRFs of the micro-milling cutter and cross FRFs between a point on the shank and one on the tool tip. Additionally, by utilizing a cutting force model specific to micro-milling, the bending stress experienced by the tool is computed, and the tool breakage curve is subsequently determined based on the material’s permissible maximum allowable stress. The FRFs of the micro-milling tool, alongside the tool breakage curve, are then integrated to generate the final stability lobe diagrams (SLDs). The effectiveness and reliability of the proposed methodology are confirmed through a comprehensive series of numerical and experimental validations. Full article
(This article belongs to the Special Issue Dynamics and Machining Stability for Flexible Systems)
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<p>A milling system with two degrees of freedom.</p> Full article ">Figure 2
<p>Micro−milling spindle–holder–tool structure.</p> Full article ">Figure 3
<p>Illustration of the micro-milling process. (<b>a</b>) Both shearing and ploughing effects. (<b>b</b>) Only ploughing effect.</p> Full article ">Figure 4
<p>Illustration of calculation of maximum bending stress.</p> Full article ">Figure 5
<p>Experimental equipment. (<b>a</b>) Equipment for excitation. (<b>b</b>) Equipment for measuring response. (<b>c</b>) Equipment for measuring excitation and response signals.</p> Full article ">Figure 6
<p>FRFs of tool tip with clamped length of 35 mm.</p> Full article ">Figure 7
<p>FRFs of tool tip with clamped length of 35 mm. (<b>a</b>) Magnitude. (<b>b</b>) Real part. (<b>c</b>) Imaginary part.</p> Full article ">Figure 8
<p>FRFs of tool tip with clamped length of 25 mm. (<b>a</b>) Magnitude. (<b>b</b>) Real part. (<b>c</b>) Imaginary part.</p> Full article ">Figure 9
<p>Maximum of bending stress of a micro−milling cutter with diameter of 1 mm.</p> Full article ">Figure 10
<p>Breakage curve of micro−milling cutter.</p> Full article ">Figure 11
<p>SLDs considering tool breakage with clamped length of 35 mm. (<b>a</b>) SLDs considering tool breakage when feed per tooth is 60 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m and the spindle speed is from 25,000 to 30,000 rpm. (<b>b</b>) SLDs considering tool breakage when feed per tooth is 120 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m and the spindle speed is from 25,000 to 30,000 rpm. (<b>c</b>) SLDs considering tool breakage when feed per tooth is 60 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m and the spindle speed is from 55,000 to 60,000 rpm. (<b>d</b>) SLDs considering tool breakage when feed per tooth is 120 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m and the spindle speed is from 55,000 to 60,000 rpm.</p> Full article ">Figure 12
<p>SLDs considering tool breakage with clamped length of 25 mm. (<b>a</b>) SLDs considering tool breakage when feed per tooth is 60 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m and the spindle speed is from 25,000 to 30,000 rpm. (<b>b</b>) SLDs considering tool breakage when feed per tooth is 120 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m and the spindle speed is from 25,000 to 30,000 rpm. (<b>c</b>) SLDs considering tool breakage when feed per tooth is 60 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m and the spindle speed is from 55,000 to 60,000 rpm. (<b>d</b>) SLDs considering tool breakage when feed per tooth is 120 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m and the spindle speed is from 55,000 to 60,000 rpm.</p> Full article ">Figure 13
<p>Acceleration signals and power spectrum filtered by VFF−RLS algorithm when the spindle speed is 25,500 rpm and the axial depths of cut are (<b>a</b>) 50 and (<b>b</b>) 150 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m.</p> Full article ">Figure 14
<p>Workpiece and micro-milling cutter used in experiments. (<b>a</b>) Workpiece. (<b>b</b>) Breaking cutter.</p> Full article ">Figure 15
<p>Cutting force signals when axial depth of cut is 400 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m and feed per tooth is 60 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m.</p> Full article ">
28 pages, 1690 KiB  
Review
Application of Microwave Energy to Biomass: A Comprehensive Review of Microwave-Assisted Technologies, Optimization Parameters, and the Strengths and Weaknesses
by Alejandra Sophia Lozano Pérez, Juan José Lozada Castro and Carlos Alberto Guerrero Fajardo
J. Manuf. Mater. Process. 2024, 8(3), 121; https://doi.org/10.3390/jmmp8030121 - 7 Jun 2024
Cited by 2 | Viewed by 2062
Abstract
This review article focuses on the application of microwave-assisted techniques in various processes, including microwave-assisted extraction, microwave-assisted pyrolysis, microwave-assisted acid hydrolysis, microwave-assisted organosolv, and microwave-assisted hydrothermal pretreatment. This article discusses the mechanisms behind these techniques and their potential for increasing yield, producing more [...] Read more.
This review article focuses on the application of microwave-assisted techniques in various processes, including microwave-assisted extraction, microwave-assisted pyrolysis, microwave-assisted acid hydrolysis, microwave-assisted organosolv, and microwave-assisted hydrothermal pretreatment. This article discusses the mechanisms behind these techniques and their potential for increasing yield, producing more selectivity, and lowering reaction times while reducing energy usage. It also highlights the advantages and disadvantages of each process and emphasizes the need for further research to scale the processes and optimize conditions for industrial applications. A specific case study is presented on the pretreatment of coffee waste, demonstrating how the choice of microwave-assisted processes can lead to different by-products depending on the initial composition of the biomass. Full article
(This article belongs to the Special Issue Sustainable Manufacturing for a Better Future)
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<p>Microwave-assisted processes.</p> Full article ">Figure 2
<p>Explanation of microwave radiation.</p> Full article ">Figure 3
<p>Different designs reported for microwave-assisted processes. (<b>a</b>) [<a href="#B129-jmmp-08-00121" class="html-bibr">129</a>], (<b>b</b>) [<a href="#B130-jmmp-08-00121" class="html-bibr">130</a>], (<b>c</b>) [<a href="#B131-jmmp-08-00121" class="html-bibr">131</a>], (<b>d</b>) [<a href="#B132-jmmp-08-00121" class="html-bibr">132</a>], and (<b>e</b>) [<a href="#B127-jmmp-08-00121" class="html-bibr">127</a>].</p> Full article ">
29 pages, 6590 KiB  
Article
Theoretical Assessment of the Environmental Impact of the Preheating Stage in Thermoplastic Composite Processing: A Step toward Sustainable Manufacturing
by Abbas Hosseini
J. Manuf. Mater. Process. 2024, 8(3), 120; https://doi.org/10.3390/jmmp8030120 - 7 Jun 2024
Viewed by 1021
Abstract
Manufacturing processes have always played a pivotal role in the life cycle assessment of products, necessitating focused efforts to minimize their impact on the environment. Thermoplastic composite manufacturing is no exception to this concern. Within thermoplastic composite manufacturing, the preheating process stands out [...] Read more.
Manufacturing processes have always played a pivotal role in the life cycle assessment of products, necessitating focused efforts to minimize their impact on the environment. Thermoplastic composite manufacturing is no exception to this concern. Within thermoplastic composite manufacturing, the preheating process stands out as one of the most energy-intensive stages, significantly affecting the environment. In this study, a theoretical analysis is conducted to compare three modes of preheating: conductive, radiative, and convective modes, considering their energy consumption and environmental impact. The analysis reveals the potential for substantial energy savings and emissions reduction through the selection of a proper preheating mode. Since the analysis used in this study is theoretical, it facilitates a parametric study of different modes of preheating to assess how process parameters impact the environment. Moreover, this study includes a comparison between emissions from material production and the preheating process, highlighting the substantial contribution of the preheating process to the overall product life cycle assessment. Full article
(This article belongs to the Special Issue Sustainable Manufacturing for a Better Future)
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<p>Identification of the initial system boundaries, representing a partial gate-to-gate LCA.</p> Full article ">Figure 2
<p>Schematic representation of the preheating process, illustrating different modes of heat transfer (conductive, radiative, and convective) in a composite laminate. The space domain of the laminate has been discretized, and a space interval of ∆<span class="html-italic">z</span> is considered.</p> Full article ">Figure 3
<p>Schematic representation of the interface between the hot plate and the composite laminate. An assumption is introduced, where the temperatures of nodes M + 1 and M − 1 are higher than that of node M, facilitating heat transfer solely to the control element.</p> Full article ">Figure 4
<p>Schematic representation of the equivalent additional contact resistance layer.</p> Full article ">Figure 5
<p>Schematic representation of the boundary layer in the convective mode of heat transfer. An assumption is introduced, where the temperatures of nodes M + 1 and M − 1 are higher than that of node M, facilitating heat transfer solely to the control element.</p> Full article ">Figure 6
<p>Schematic representation of the infrared heater and composite laminate configuration.</p> Full article ">Figure 7
<p>Schematic representation of the infrared heater and composite laminate configuration for the calculation of interior nodal temperature profile. An assumption is made in which heat only flows toward the control element.</p> Full article ">Figure 8
<p>MATLAB finite difference simulation results vs. experimental results for the conductive mode of preheating. Experimental results reprinted from Compos. Part A, 28, Cunningham, J.E.; Monaghan, P.F.; Brogan, M.T.; Cassidy, S.F., Modelling of pre-heating of flat panels prior to press forming, 17–24, 1997, with permission from Elsevier.</p> Full article ">Figure 9
<p>MATLAB finite difference simulation results vs. experimental results for the convective mode of preheating. Experimental results reprinted from Compos. Part A, 28, Cunningham, J.E.; Monaghan, P.F.; Brogan, M.T.; Cassidy, S.F., Modelling of pre-heating of flat panels prior to press forming, 17–24, 1997, with permission from Elsevier.</p> Full article ">Figure 10
<p>MATLAB finite difference simulation results vs. experimental results for the radiative mode of preheating. Experimental results reprinted from Composites Part C: Open Access, 5, Nardi, D.; Sinke, J., Design analysis for thermoforming of thermoplastic composites: prediction and machine learning-based optimization, 100126, 2021.</p> Full article ">Figure 11
<p>MATLAB temperature profile prediction of the composite laminate using the parameters listed in <a href="#jmmp-08-00120-t005" class="html-table">Table 5</a> (Conductive mode of preheating).</p> Full article ">Figure 12
<p>The mechanism of heat transfer in the hot plate mold.</p> Full article ">Figure 13
<p>MATLAB temperature profile prediction of the composite laminate using the parameters listed in <a href="#jmmp-08-00120-t007" class="html-table">Table 7</a> (Convective mode of preheating).</p> Full article ">Figure 14
<p>MATLAB temperature profile prediction of the composite laminate using the parameters listed in <a href="#jmmp-08-00120-t009" class="html-table">Table 9</a> (Radiative mode of preheating).</p> Full article ">Figure 15
<p>Schematic demonstration of effect-to-emission relationship.</p> Full article ">Figure 16
<p>Process parameter sensitivity analysis: (<b>a</b>) Conductive, (<b>b</b>) Convective, and (<b>c</b>) Radiative mode of preheating. Please note that to better illustrate the sensitivity of the preheating process to each parameter, the graphs are drawn based on a logarithmic modified sensitivity index approach. That is, the shown SI for each parameter is equal to Log (SI + A small positive constant offset). However, the SI values reported for each parameter on the axes of the graphs are the actual sensitivity indices.</p> Full article ">Figure 17
<p>Laminate thickness analysis: (<b>a</b>) The trend of energy consumption for different preheating modes versus laminate thickness, (<b>b</b>) Normalized energy consumption for laminates with different thicknesses referenced to the conductive mode.</p> Full article ">
13 pages, 4017 KiB  
Article
Effects of Oil Concentration in Flood Cooling on Cutting Force, Tool Wear and Surface Roughness in GTD-111 Nickel-Based Superalloy Slot Milling
by Gábor Kónya and Zsolt F. Kovács
J. Manuf. Mater. Process. 2024, 8(3), 119; https://doi.org/10.3390/jmmp8030119 - 7 Jun 2024
Viewed by 869
Abstract
Cooling–lubricating processes have a big impact on cutting force, tool wear, and the quality of the machined surface, especially for hard-to-machine superalloys, so the choice of the right cooling–lubricating method is of great importance. Nickel-based superalloys are among the most difficult materials to [...] Read more.
Cooling–lubricating processes have a big impact on cutting force, tool wear, and the quality of the machined surface, especially for hard-to-machine superalloys, so the choice of the right cooling–lubricating method is of great importance. Nickel-based superalloys are among the most difficult materials to machine due to their high hot strength, work hardening, and extremely low thermal conductivity. Previous research has shown that flood cooling results in the least tool wear and cutting force among different cooling–lubricating methods. Thus, the effects of the flood oil concentration (3%; 6%; 9%; 12%; and 15%) on the above-mentioned factors were investigated during the slot milling of the GTD-111 nickel-based superalloy. The cutting force was measured during machining with a Kistler three-component dynamometer, and then after cutting the tool wear and the surface roughness on the bottom surface of the milled slots were measured with a confocal microscope and tactile roughness tester. The results show that at a 12% oil concentration, the tool load and tool wear are the lowest; even at an oil concentration of 15%, a slight increase is observed in both factors. Essentially, a higher oil concentration reduces friction between the tool and the workpiece contact surface, resulting in reduced tool wear and cutting force. Furthermore, due to less friction, the heat generation in the cutting zone is also reduced, resulting in a lower heat load on the tool, which increases tool life. It is interesting to note that the 6% oil concentration had the highest cutting force and tool wear, and strong vibration was heard during machining, which is also reflected in the force signal. The change in oil concentration did not effect the surface roughness. Full article
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<p>Factors affecting machinability.</p> Full article ">Figure 2
<p>Experimental procedure.</p> Full article ">Figure 3
<p>(<b>a</b>) <span class="html-italic">F</span><sub>x</sub>; (<b>b</b>) <span class="html-italic">F</span><sub>y</sub>; (<b>c</b>) <span class="html-italic">F</span><sub>z</sub>; and (<b>d</b>) <span class="html-italic">F</span> resulting cutting force as a function of machining time for an axial depth of cut of 8 mm.</p> Full article ">Figure 4
<p>Broken tool at 3% emulsion concentration.</p> Full article ">Figure 5
<p>(<b>a</b>) <span class="html-italic">F</span><sub>x</sub>; (<b>b</b>) <span class="html-italic">F</span><sub>y</sub>; (<b>c</b>) <span class="html-italic">F</span><sub>z</sub>; and (<b>d</b>) resulting cutting force as a function machining time for an axial depth of cut of 4 mm.</p> Full article ">Figure 6
<p>Main cutting force as a function of oil concentration.</p> Full article ">Figure 7
<p>Microscopic images of the tools used at (<b>a</b>) 3%; (<b>b</b>) 6%; (<b>c</b>) 9%; (<b>d</b>) 12%; and (<b>e</b>) 15% oil concentrations.</p> Full article ">Figure 8
<p>Tool wear as a function of oil concentration.</p> Full article ">Figure 9
<p>Average surface roughness (<span class="html-italic">R</span><sub>a</sub>) and main roughness depth (<span class="html-italic">R</span><sub>z</sub>) as a function of the oil concentration.</p> Full article ">
18 pages, 9340 KiB  
Article
Fused Filament Fabrication of WC-10Co Hardmetals: A Study on Binder Formulations and Printing Variables
by Julián David Rubiano Buitrago, Andrés Fernando Gil Plazas, Luis Alejandro Boyacá Mendivelso and Liz Karen Herrera Quintero
J. Manuf. Mater. Process. 2024, 8(3), 118; https://doi.org/10.3390/jmmp8030118 - 31 May 2024
Viewed by 958
Abstract
This research explores the utilization of powder fused filament fabrication (PFFF) for producing tungsten carbide-cobalt (WC-10Co) hardmetals, focusing on binder formulations and their impact on extrusion force as well as the influence of printing variables on the green and sintered density of samples. [...] Read more.
This research explores the utilization of powder fused filament fabrication (PFFF) for producing tungsten carbide-cobalt (WC-10Co) hardmetals, focusing on binder formulations and their impact on extrusion force as well as the influence of printing variables on the green and sintered density of samples. By examining the interplay between various binder compositions and backbone contents, this study aims to enhance the mechanical properties of the sintered parts while reducing defects inherent in the printing process. Evidence suggests that formulated feedstocks affect the hardness of the sintered hardmetal—not due to microstructural changes but macrostructural responses such as macro defects introduced during printing, debinding, and sintering of samples. The results demonstrate the critical role of polypropylene grafted with maleic anhydride (PP-MA) content in improving part density and sintered hardness, indicating the need for tailored thermal debinding protocols tailored to each feedstock. This study provides insights into feedstock formulation for hardmetal PFFF, proposing a path toward refining manufacturing processes to achieve better quality and performance of 3D printed hardmetal components. Full article
(This article belongs to the Special Issue High-Performance Metal Additive Manufacturing)
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<p>DSC curves of the following: (<b>a</b>) raw polymers, binder systems and binder systems mixed with WC-10Co powders; (<b>b</b>) magnification of binder systems mixed with the powders.</p> Full article ">Figure 2
<p>TGA results of the studied feedstocks: (<b>a</b>) mass-loss temperature diagram; (<b>b</b>) derivative mass calculation.</p> Full article ">Figure 3
<p>Force measurements for manufactured feedstocks: (<b>a</b>) variation with temperature and extrusion speed; (<b>b</b>) test at ambient temperature leading to filament failure.</p> Full article ">Figure 4
<p>Relative green densities for different feedstocks and printing temperatures.</p> Full article ">Figure 5
<p>SEM-BSE details of some defects and optical microscopy of the green samples after cryogenic fracture of different feedstocks: (<b>a</b>–<b>d</b>) M1-P, M2-P, M3-P, M4-P respectively, at 220 °C and 10 mm/s; (<b>e</b>–<b>h</b>) M1-P, M2-P, M3-P, M4-P respectively, printed at 200 °C and 10 mm/s; (<b>i</b>–<b>l</b>) M1-P, M2-P, M3-P, M4-P respectively, printed at 200 °C and 7.5 mm/s.</p> Full article ">Figure 6
<p>Relative densities of the samples printed at 200 °C at different speeds: (<b>a</b>) green state, (<b>b</b>) after sintering.</p> Full article ">Figure 7
<p>Metallographic images of representative results of the sintered samples: (<b>a</b>) printed with the M1-P feedstock at 200 °C and 10 mm/s, (<b>b</b>) printed with the M2-P feedstock at 200 °C and 10 mm/s, (<b>c</b>) 200× micrograph showing carbon precipitation and micropores found in all the samples. The 200× micrographs of the other samples printed at 200 °C can be found in the Supplementary Material.</p> Full article ">Figure 8
<p>Backscattered SEM micrographs of the sintered samples printed at 200 °C made with the developed feedstocks and centered in the carbon precipitated areas printed at different speeds: (<b>a</b>) M1-P at 10 mm/s, (<b>b</b>) M1-P at 7.5 mm/s, (<b>c</b>) M2-P at 10 mm/s, (<b>d</b>) M2-P at 7.5 mm/s, (<b>e</b>) M3-P at 10 mm/s, (<b>f</b>) M3-P at 7.5 mm/s, (<b>g</b>) M4-P at 10 mm/s, and (<b>h</b>) M4-P at 7.5 mm/s.</p> Full article ">Figure 9
<p>Developed feedstocks after printing at 200 °C and sintering at 1500 °C: (<b>a</b>) Violin plot of grain size from data obtained with digital image processing; (<b>b</b>) Vickers hardness.</p> Full article ">Figure 10
<p>Persistence of printing defects after the sintering process: (<b>a</b>) mountain-like defect found in M2-P feedstock; (<b>b</b>) lack of filling between internal infill lines and the outer layer found in M4-P Feedstock; (<b>c</b>) entrapped gas found in M3-P feedstock.</p> Full article ">
26 pages, 14300 KiB  
Article
SolDef_AI: An Open Source PCB Dataset for Mask R-CNN Defect Detection in Soldering Processes of Electronic Components
by Gianmauro Fontana, Maurizio Calabrese, Leonardo Agnusdei, Gabriele Papadia and Antonio Del Prete
J. Manuf. Mater. Process. 2024, 8(3), 117; https://doi.org/10.3390/jmmp8030117 - 31 May 2024
Cited by 1 | Viewed by 1528
Abstract
The soldering process for aerospace applications follows stringent requirements and standards to ensure the reliability and safety of electronic connections in aerospace systems. For this reason, the quality control phase plays an important role to guarantee requirements compliance. This process often requires manual [...] Read more.
The soldering process for aerospace applications follows stringent requirements and standards to ensure the reliability and safety of electronic connections in aerospace systems. For this reason, the quality control phase plays an important role to guarantee requirements compliance. This process often requires manual control since technicians’ knowledge is fundamental to obtain effective quality check results. In this context, the authors have developed a new open source dataset (SolDef_AI) to implement an innovative methodology for printed circuit board (PCB) defect detection exploiting the Mask R-CNN algorithm. The presented open source dataset aims to overcome the challenges associated with the availability of datasets for model training in this specific research and electronics industrial field. The dataset is open source and available online. Full article
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<p>The vision system designed to acquire images for the SolDef_AI dataset. Setup for (<b>i</b>) top view, (<b>ii</b>) 45-degree view, and (<b>iii</b>) axonometric view of the soldered SMT components.</p> Full article ">Figure 2
<p>This is a figure. Schemes follow the same formatting.</p> Full article ">Figure 3
<p>Top view of the component: (<b>a</b>) correctly soldered on the PCB; (<b>b</b>) not correctly soldered on the PCB.</p> Full article ">Figure 4
<p>Lateral view of components with (<b>a</b>) the correct quantity of solder material, (<b>b</b>) an excessive quantity of soldering material, (<b>c</b>) an insufficient quantity of soldering material, and (<b>d</b>) the presence of spikes.</p> Full article ">Figure 5
<p>Sample of images included in dataset_1.</p> Full article ">Figure 6
<p>Sample of images included in dataset_2.</p> Full article ">Figure 7
<p>Incorrect positioning of the component (1)—no_good label.</p> Full article ">Figure 8
<p>Incorrect positioning of the component (2)—no_good label.</p> Full article ">Figure 9
<p>Correct positioning of the component (1)—good label.</p> Full article ">Figure 10
<p>Correct positioning of the component (2)—good label.</p> Full article ">Figure 11
<p>Labeling of a good solder joint.</p> Full article ">Figure 12
<p>Labeling of an excessive solder joint.</p> Full article ">Figure 13
<p>Labeling of a poor solder.</p> Full article ">Figure 14
<p>Labeling of a solder joint with spike.</p> Full article ">Figure 15
<p>Mask R-CNN configuration.</p> Full article ">Figure 16
<p>Loss function trends for classification (<b>a</b>), detection (<b>b</b>), segmentation (<b>c</b>), and total loss (<b>d</b>)—dataset_1.</p> Full article ">Figure 17
<p>Inference example 1—dataset_1.</p> Full article ">Figure 18
<p>Inference example 2—dataset_1.</p> Full article ">Figure 19
<p>Inference example 3—dataset_1.</p> Full article ">Figure 20
<p>Inference example 4—dataset_1.</p> Full article ">Figure 21
<p>Loss function trends for classification (<b>a</b>), detection (<b>b</b>), segmentation (<b>c</b>), and total loss (<b>d</b>)—dataset_2.</p> Full article ">Figure 22
<p>Inference example 1—dataset_2.</p> Full article ">Figure 23
<p>Inference example 2—dataset_2.</p> Full article ">Figure 24
<p>Inference example 3—dataset_2.</p> Full article ">Figure 25
<p>Inference example 4—dataset_2.</p> Full article ">Figure 26
<p>Inference example 5—dataset_2.</p> Full article ">Figure 27
<p>Inference example 6—dataset_2.</p> Full article ">
22 pages, 8399 KiB  
Article
Process Optimization and Distortion Prediction in Directed Energy Deposition
by Adem Ben Hammouda, Hatem Mrad, Haykel Marouani, Ahmed Frikha and Tikou Belem
J. Manuf. Mater. Process. 2024, 8(3), 116; https://doi.org/10.3390/jmmp8030116 - 30 May 2024
Viewed by 713
Abstract
Directed energy deposition (DED), a form of additive manufacturing (AM), is gaining traction for its ability to produce complex metal parts with precise geometries. However, defects like distortion, residual stresses, and porosity can compromise part quality, leading to rejection. This research addresses this [...] Read more.
Directed energy deposition (DED), a form of additive manufacturing (AM), is gaining traction for its ability to produce complex metal parts with precise geometries. However, defects like distortion, residual stresses, and porosity can compromise part quality, leading to rejection. This research addresses this challenge by emphasizing the importance of monitoring process parameters (overlayer distance, powder feed rate, and laser path/power/spot size) to achieve desired mechanical properties. To improve DED quality and reliability, a numerical approach is presented and compared with an experimental work. The parametric finite element model and predictive methods are used to quantify and control material behavior, focusing on minimizing residual stresses and distortions. Numerical simulations using the Abaqus software 2022 are validated against experimental results to predict distortion and residual stresses. A coupled thermomechanical analysis model is employed to understand the impact of thermal distribution on the mechanical responses of the parts. Finally, new strategies based on laser scan trajectory and power are proposed to reduce residual stresses and distortions, ultimately enhancing the quality and reliability of DED-manufactured parts. Full article
(This article belongs to the Special Issue Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing)
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<p>The rotary Gaussian model.</p> Full article ">Figure 2
<p>Distribution of the Goldak model.</p> Full article ">Figure 3
<p>Model geometry: clamp, substrate, and part to fabricate.</p> Full article ">Figure 4
<p>Location of sensors.</p> Full article ">Figure 5
<p>Laser path during the deposition. (<b>a</b>) Layer n: Laser path right to left, (<b>b</b>) Layer n + 1: Laser path left to right.</p> Full article ">Figure 6
<p>Flowchart of a thermomechanical simulation.</p> Full article ">Figure 7
<p>Material activation.</p> Full article ">Figure 8
<p>The geometry of the model and the finite element mesh.</p> Full article ">Figure 9
<p>Internal working principle of deformation.</p> Full article ">Figure 10
<p>Evolution of temperature versus time of the numerical model.</p> Full article ">Figure 11
<p>Validation of the temperature of the numerical model.</p> Full article ">Figure 12
<p>Displacement distribution.</p> Full article ">Figure 13
<p>Evolution of distortion in numerical, experimental, and Abaqus benchmark models.</p> Full article ">Figure 14
<p>Stress distribution.</p> Full article ">Figure 15
<p>Laser path of the Zig-Zag strategy.</p> Full article ">Figure 16
<p>Temperature evolution with time.</p> Full article ">Figure 17
<p>Influence of strategy on the temperature of the numerical model.</p> Full article ">Figure 18
<p>Distribution of distortion.</p> Full article ">Figure 19
<p>Influence of the strategy on the distribution of numerical model distortion.</p> Full article ">Figure 20
<p>Stress distribution of this strategy.</p> Full article ">Figure 21
<p>Temperature evolution concerning time.</p> Full article ">Figure 22
<p>Influence of the low-power strategy on numerical model temperature.</p> Full article ">Figure 23
<p>Distribution of distortion after cooling.</p> Full article ">Figure 24
<p>Effect of the strategy on the distortion distribution up to 30 min.</p> Full article ">Figure 25
<p>Stress distribution.</p> Full article ">
26 pages, 8146 KiB  
Article
A Comparative Study of Different Milling Strategies on Productivity, Tool Wear, Surface Roughness, and Vibration
by Francisco J. G. Silva, Rui P. Martinho, Luís L. Magalhães, Filipe Fernandes, Rita C. M. Sales-Contini, Luís M. Durão, Rafaela C. B. Casais and Vitor F. C. Sousa
J. Manuf. Mater. Process. 2024, 8(3), 115; https://doi.org/10.3390/jmmp8030115 - 30 May 2024
Viewed by 1044
Abstract
Strategies for obtaining deep slots in soft materials can vary significantly. Conventionally, the tool travels along the slot, removing material mainly with the side cutting edges. However, a “plunge milling” strategy is also possible, performing the cut vertically, taking advantage of the tip [...] Read more.
Strategies for obtaining deep slots in soft materials can vary significantly. Conventionally, the tool travels along the slot, removing material mainly with the side cutting edges. However, a “plunge milling” strategy is also possible, performing the cut vertically, taking advantage of the tip cutting edges that almost reach the center of the tool. Although both strategies are already commonly used, there is a clear gap in the literature in studies that compare tool wear, surface roughness, and productivity in each case. This paper describes an experimental study comparing the milling of deep slots in AA7050-T7451 aluminum alloy, coated with a novel DLCSiO500W3.5O2 layer to minimize the aluminum adhesion to the tool, using conventional and plunge milling strategies. The main novelty of this paper is to present a broad study regarding different factors involved in machining operations and comparing two distinct strategies using a novel tool coating in the milling of aeronautical aluminum alloy. Tool wear is correlated with the vibrations of the tools in each situation, the cycle time is compared between the cases studied, and the surface roughness of the machined surfaces is analyzed. This study concludes that the cycle time of plunge milling can be about 20% less than that of conventional milling procedures, favoring economic sustainability and modifying the wear observed on the tools. Plunge milling can increase productivity, does not increase tool tip wear, and avoids damaging the side edges of the tool, which can eventually be used for final finishing operations. Therefore, it can be said that the plunge milling strategy improves economic and environmental sustainability as it uses all the cutting edges of the tools in a more balanced way, with less global wear. Full article
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<p>(<b>a</b>) View of the cutting tool used in the milling tests; (<b>b</b>) top view of the milling tools used in this work.</p> Full article ">Figure 2
<p>(<b>a</b>) Machined part front view after plunge milling and finishing operations; (<b>b</b>) conventional strategy scheme: <span class="html-italic">V<sub>c</sub></span> = 160 m/min, <span class="html-italic">f<sub>n</sub></span> = 0.14 mm/rev, <span class="html-italic">a<sub>p</sub></span> = 3.7 mm; (<b>c</b>) plunge milling strategy scheme: <span class="html-italic">V<sub>c</sub></span> = 160 m/min, <span class="html-italic">f<sub>n</sub></span> = 0.14 mm/rev, <span class="html-italic">a<sub>e</sub></span> = 4.8 mm; and (<b>d</b>) finishing strategy scheme for both machining strategies: <span class="html-italic">V<sub>c</sub></span> = 160 m/min, <span class="html-italic">f<sub>n</sub></span> = 0.07 mm/rev, <span class="html-italic">a<sub>p</sub></span> = 14.8 mm. (Red arrows: movements).</p> Full article ">Figure 3
<p>Roughness measurement position on the sample surface: start of finishing position (1) and end of finishing position (6) measured at the lower and upper positions, respectively. Points from 7 to 12 represent the sample’s width-the start of the finishing position (7) and the end of the finishing position (12).</p> Full article ">Figure 4
<p>Machining setup used for plunge and conventional milling: (a) triaxial accelerometer used to measure vibrational signal and (b) AA7050-T7451 blocks.</p> Full article ">Figure 5
<p>SEM micrograph showing the DLCSiO500W3.5O<sub>2</sub> film thickness deposited by PVD magnetron sputtering: (<b>a</b>) coating and interlayer dimensions, (<b>b</b>) sampling area for EDS analysis, (<b>c</b>) EDS spectrum from sampling area Z1, (<b>d</b>) EDS spectrum from sampling area Z2, and (<b>e</b>) EDS spectrum from sampling area Z3.</p> Full article ">Figure 6
<p>SEM micrograph analysis of a side of the tool cutting edge used in the plunge milling strategy (PM): (<b>a</b>) a general overview of the lateral part of the cutting edge; (<b>b</b>) a detailed view of the cutting-edge tip.</p> Full article ">Figure 7
<p>SEM micrograph analysis of a tool cutting edge used in plunge milling strategy (PM): (<b>a</b>) top view of one of the teeth; (<b>b</b>) lateral view of the chipping effect in one of the three teeth; (<b>c</b>) EDS analysis of the tool surface.</p> Full article ">Figure 7 Cont.
<p>SEM micrograph analysis of a tool cutting edge used in plunge milling strategy (PM): (<b>a</b>) top view of one of the teeth; (<b>b</b>) lateral view of the chipping effect in one of the three teeth; (<b>c</b>) EDS analysis of the tool surface.</p> Full article ">Figure 8
<p>Side view of SEM micrograph analysis of a tool cutting edge used in conventional milling strategy (TM): (<b>a</b>) general overview of a cutting edge; (<b>b</b>) detailed view of the cutting edge.</p> Full article ">Figure 9
<p>SEM micrograph analysis of a tool cutting edge used in the conventional milling strategy (TM): (<b>a</b>) top view of one of the teeth; (<b>b</b>) lateral view of the chipping effect in one of the three teeth; (<b>c</b>) EDS analysis of the tool surface.</p> Full article ">Figure 10
<p>Vibrational analysis of the plunge-milling roughing process (Blue line: X velocity (RMS [mm/s]); Yellow line: Y velocity (RMS [mm/s]); Green line: Z velocity (RMS [mm/s])).</p> Full article ">Figure 11
<p>(<b>a</b>) Vibrational analysis of the plunge-milling finishing process (Blue line: X velocity (RMS [mm/s]); Yellow line: Y velocity (RMS [mm/s]); Green line: Z velocity (RMS [mm/s])); (<b>b</b>) Finishing milling scheme: front view.</p> Full article ">Figure 12
<p>Vibrational analysis of the conventional roughing process (Blue line: X velocity (RMS [mm/s]); Yellow line: Y velocity (RMS [mm/s]); Green line: Z velocity (RMS [mm/s])).</p> Full article ">
19 pages, 9216 KiB  
Article
Monitoring Variability in Melt Pool Spatiotemporal Dynamics (VIMPS): Towards Proactive Humping Detection in Additive Manufacturing
by Mohamed Abubakr Hassan, Mahmoud Hassan, Chi-Guhn Lee and Ahmad Sadek
J. Manuf. Mater. Process. 2024, 8(3), 114; https://doi.org/10.3390/jmmp8030114 - 29 May 2024
Cited by 2 | Viewed by 1041
Abstract
Humping is a common defect in direct energy deposition processes that reduces the geometric integrity of printed products. The available literature on humping detection is deemed reactive, as they focus on detecting late-stage melt pool spatial abnormalities. Therefore, this work introduces a novel, [...] Read more.
Humping is a common defect in direct energy deposition processes that reduces the geometric integrity of printed products. The available literature on humping detection is deemed reactive, as they focus on detecting late-stage melt pool spatial abnormalities. Therefore, this work introduces a novel, proactive indicator designed to detect early-stage spatiotemporal abnormalities. Specifically, the proposed indicator monitors the variability of instantaneous melt pool solidification-front speed (VIMPS). The solidification front dynamics quantify the intensity of cyclic melt pool elongation induced by early-stage humping. VIMPS tracks the solidification front dynamics based on the variance in the melt pool infrared radiations. Qualitative and quantitive analysis of the collected infrared data confirms VIMPS’s utility in reflecting the intricate humping-induced dynamics and defects. Experimental results proved VIMPS’ proactivity. By capturing early spatiotemporal abnormalities, VIMPS predicted humping by up to 10 s before any significant geometric defects. In contrast, current spatial abnormality-based methods failed to detect humping until 20 s after significant geometric defects had occurred. VIMPS’ proactive detection capabilities enable effective direct energy deposition control, boosting the process’s productivity and quality. Full article
(This article belongs to the Special Issue Advances in Directed Energy Deposition Additive Manufacturing)
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<p>(<b>a</b>) Real image of severe humping redrawn from [<a href="#B8-jmmp-08-00114" class="html-bibr">8</a>] showing the crests and valleys forming the wavy structure. Each column (i.e., from (<b>b</b>–<b>f</b>)) shows different melt pool modes with humping severity increasing from (<b>b</b>) to (<b>f</b>). Within each column, time progresses from top to bottom.</p> Full article ">Figure 2
<p>Laser profile used.</p> Full article ">Figure 3
<p>(<b>a</b>) Schematic and (<b>b</b>) actual view of the experimental setup.</p> Full article ">Figure 4
<p>The melt pool and the approximate contour of the heat-affected zone (HAZ).</p> Full article ">Figure 5
<p>The progression of melt pool solidification after the laser was turned off. Note: t = 303.75 s corresponds to the moment at which the laser was turned off.</p> Full article ">Figure 6
<p>(<b>d</b>) Average pixel intensity (API) variations during the accumulation (<b>a</b>→<b>b</b>) and solidification (<b>b</b>→<b>c</b>) through one elongation mode cycle.</p> Full article ">Figure 7
<p>As-printed parts. (<b>a</b>,<b>d</b>) Test-1, (<b>b</b>,<b>e</b>) Test-2, and (<b>c</b>,<b>f</b>) Test-3. (<b>d</b>–<b>f</b>) Top-view with an elevation map (i.e., topography). Yellow regions are higher than blue regions by 1 mm, as shown in the scale. Note that the variations in (<b>e</b>,<b>f</b>) are mainly due to the helical printing path, while in (<b>d</b>), the variations are dominated by the humping-induced crests.</p> Full article ">Figure 8
<p>Variability of instantaneous melt pool solidification-front speed (VIMPS) for different prints. (<b>a</b>) Test #1 (<b>b</b>) Test #2 (<b>c</b>) Test #3.</p> Full article ">Figure 9
<p>(<b>a</b>) Variability of instantaneous melt pool solidification-front speed (VIMPS) overlayed over the as-printed part’s surface topography. (<b>b</b>) Side view of the as-printed part, highlighting the scanned surface topography shown in subfigure (<b>a</b>) in the same Figure.</p> Full article ">Figure 10
<p>Manually annotated melt pool at the detachment mode (<b>a</b>,<b>b</b>) from Test #1 and (<b>c</b>,<b>d</b>) from Test #3. (<b>a</b>,<b>c</b>) First instances of the detachment mode in the corresponding test and (<b>b</b>,<b>d</b>) the second instance.</p> Full article ">Figure 11
<p>(<b>a</b>) Average pixel intensity (API) during tilting in Test #1 (i.e., layer-32 to layer-44) and (<b>b</b>) its fast Fourier transformation analysis.</p> Full article ">Figure 12
<p>Micro-protrusion during Test #2. (<b>a</b>) A strip from the side wall, (<b>b</b>,<b>c</b>) topography of a small protrusion, and the entire strip, respectively.</p> Full article ">Figure 13
<p>(<b>a</b>) Early and (<b>b</b>) late infrared images from test #1 and (<b>c</b>) late test #2. The accumulation step begins in (1) and ends in (2), and the solidification step starts in (2) and ends in (3). Subplots (4) show the pixel intensity increase during the solidification (i.e., the pixel-wise difference between (3) and (2). Subplots (5) show the average pixel intensity (API) and variability of instantaneous melt pool solidification-front speed (VIMPS) variation during the elongation cycle.</p> Full article ">Figure A1
<p>(<b>a</b>) MP (melt pool) infrared image during detachment mode, the same as in <a href="#jmmp-08-00114-f010" class="html-fig">Figure 10</a>a. (<b>b</b>–<b>i</b>) SAM (Segment Anything Model) segmentation results. (<b>b</b>) Zero-shot segmentation results without human aid, (<b>c</b>) the results with human aid that lead to correct segmentation, and (<b>d</b>–<b>f</b>) the intermediate steps between (<b>b</b>,<b>c</b>). Interested readers can reproduce this experiment by downloading (<b>a</b>) from the <a href="#app1-jmmp-08-00114" class="html-app">Supplementary Material</a> and testing the SAM demo on the following link [<a href="#B35-jmmp-08-00114" class="html-bibr">35</a>]. (<b>b</b>) Can be obtained using the automatic “Everything” prompting, but (<b>c</b>–<b>f</b>) needs more sophisticated prompting with interactive points using the manual “Hover and Click”.</p> Full article ">
19 pages, 33144 KiB  
Article
Performance Analysis of Helical Milling and Drilling Operations While Machining Carbon Fiber-Reinforced Aluminum Laminates
by Gururaj Bolar, Anoop Aroor Dinesh, Ashwin Polishetty, Raviraj Shetty, Anupama Hiremath and V. L. Neelakantha
J. Manuf. Mater. Process. 2024, 8(3), 113; https://doi.org/10.3390/jmmp8030113 - 29 May 2024
Viewed by 897
Abstract
Being a difficult-to-cut material, Fiber Metal Laminates (FML) often pose challenges during conventional drilling and require judicious selection of machining parameters to ensure defect-free laminates that can serve reliably during their service lifetime. Helical milling is a promising technique for producing good-quality holes [...] Read more.
Being a difficult-to-cut material, Fiber Metal Laminates (FML) often pose challenges during conventional drilling and require judicious selection of machining parameters to ensure defect-free laminates that can serve reliably during their service lifetime. Helical milling is a promising technique for producing good-quality holes and is preferred over conventional drilling. The paper compares conventional drilling with the helical milling technique for producing holes in carbon fiber-reinforced aluminum laminates. The effect of machining parameters, such as cutting speed and axial feed, on the magnitude of cutting force and the machining temperature during conventional drilling as well as helical milling is studied. It was observed that the thrust force produced during machining reduces considerably during helical milling in comparison to conventional drilling at a constant axial feed rate. The highest machining temperature recorded for helical milling was much lower in comparison to the highest machining temperature measured during conventional drilling. The machining temperatures recorded during helical milling were well below the glass transition temperature of the epoxy used in carbon fiber prepreg, hence protecting the prepreg from thermal degradation during the hole-making process. The surface roughness of the holes produced by both techniques is measured, and the surface morphology of the drilled holes is analyzed using a scanning electron microscope. The surface roughness of the helical-milled holes was lower than that for holes produced by conventional drilling. Scanning electron microscope images provided insights into the interaction of the hole surface with the chips during the chip evacuation stage under different speeds and feed rates. The microhardness of the aluminum layers increased after processing holes using drilling and helical milling operations. The axial feed/axial pitch had minimal influence on the microhardness increase in comparison to the cutting speed. Full article
(This article belongs to the Topic Advanced Composites Manufacturing and Plastics Processing)
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<p>(<b>a</b>) Surface of aluminum sheet subjected to sulfuric acid anodizing; (<b>b</b>) specimen fabrication setup; (<b>c</b>) material stacking sequence; and (<b>d</b>) fabricated FML specimen.</p> Full article ">Figure 2
<p>(<b>a</b>) Machining setup; (<b>b</b>) cutting tools for experimentation.</p> Full article ">Figure 3
<p>(<b>a</b>) Thrust force measurement; (<b>b</b>) surface roughness measurement; (<b>c</b>) microhardness measurement; (<b>d</b>) machining temperature measurement; (<b>e</b>) surface morphology imagining.</p> Full article ">Figure 4
<p>Thrust force variation with process variables in (<b>a</b>) drilling and (<b>b</b>) helical milling.</p> Full article ">Figure 5
<p>Raw force signal for (<b>a</b>) drilling and (<b>b</b>) helical milling.</p> Full article ">Figure 6
<p>Machining temperature variation with process variables in (<b>a</b>) drilling and (<b>b</b>) helical milling.</p> Full article ">Figure 7
<p>Comparison of machining temperature cartographs obtained for Case 7 during (<b>a</b>) drilling and (<b>b</b>) helical milling.</p> Full article ">Figure 8
<p>Surface roughness variation with process variables in (<b>a</b>) drilling and (<b>b</b>) helical milling.</p> Full article ">Figure 9
<p>Fiber pull-out was observed at higher feed conditions during drilling.</p> Full article ">Figure 10
<p>Comparison of surface roughness profiles obtained for Case 7 during (<b>a</b>) drilling and (<b>b</b>) helical milling.</p> Full article ">Figure 11
<p>Surface damages in the form of (<b>a</b>) grooves, (<b>b</b>) fiber pullout, (<b>c</b>) feed marks, (<b>d</b>) material smearing, (<b>e</b>) material ingression, and (<b>f</b>) material adhesion were observed in the drilled holes.</p> Full article ">Figure 12
<p>Surface damages in the form of (<b>a</b>) material adhesion, (<b>b</b>) material smearing, (<b>c</b>) feed marks, and (<b>d</b>) fiber peeling were observed in the helical milled holes.</p> Full article ">Figure 13
<p>Holes with good surface finish were observed during (<b>a</b>) drilling and (<b>b</b>) helical milling.</p> Full article ">Figure 14
<p>(<b>a</b>) Average microhardness vs. process variables for drilling operation; (<b>b</b>–<b>d</b>) microhardness at three different layers of the CARALL stack vs. process variables for drilling operation.</p> Full article ">Figure 15
<p>(<b>a</b>) Average microhardness vs. process variables for milling operation; (<b>b</b>–<b>d</b>) microhardness at three different layers of the CARALL stack vs. process variables for milling operation.</p> Full article ">
16 pages, 13981 KiB  
Article
Electrical Smoothing of the Powder Bed Surface in Laser-Based Powder Bed Fusion of Metals
by Andreas Hofmann, Tim Grotz, Nico Köstler, Alexander Mahr and Frank Döpper
J. Manuf. Mater. Process. 2024, 8(3), 112; https://doi.org/10.3390/jmmp8030112 - 28 May 2024
Cited by 1 | Viewed by 969
Abstract
Achieving a homogeneous and uniform powder bed surface as well as a defined, uniform layer thickness is crucial for achieving reproducible component properties that meet requirements when powder bed fusion of metals with a laser beam. The existing recoating processes cause wear of [...] Read more.
Achieving a homogeneous and uniform powder bed surface as well as a defined, uniform layer thickness is crucial for achieving reproducible component properties that meet requirements when powder bed fusion of metals with a laser beam. The existing recoating processes cause wear of the recoater blade due to protruded, melted obstacles, which affects the powder bed surface quality locally. Impairments to the powder bed surface quality have a negative effect on the resulting component properties such as surface quality and relative density. This can lead either to scrapped components or to additional work steps such as surface reworking. In this work, an electric smoother is presented with which a wear-free and contactless smoothing of the powder bed can be realized. The achievable powder bed surface quality was analyzed using optical profilometry. It was found that the electric smoother can compensate for impairments in the powder bed surface and achieve a reproducible surface quality of the powder bed regardless of the initial extent of the impairments. Consequently, the electric smoother offers a promising opportunity to reduce the scrap rate in PBF-LB/M and to increase component quality. Full article
(This article belongs to the Special Issue Design, Processes and Materials for Additive Manufacturing)
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<p>Schematic representation of the PBF-LB/M manufacturing process.</p> Full article ">Figure 2
<p>Example of damage to a recoater blade.</p> Full article ">Figure 3
<p>Grooves transferred to the powder bed surface by damage to the recoater blade (<b>a</b>) after exposure via laser beam and (<b>b</b>) after powder application.</p> Full article ">Figure 4
<p>Scrap components with surface damage due to unsuitable powder application by a damaged recoater blade.</p> Full article ">Figure 5
<p>Schematic representation of the functionality of the electric smoother.</p> Full article ">Figure 6
<p>Experimental setup: modified MEX machine with attached electric smoother.</p> Full article ">Figure 7
<p>Particle size distribution of the used MetcoAdd Ti64 metal powder.</p> Full article ">Figure 8
<p>Scanning electron micrographs of the used MetcoAdd Ti64 metal powder at (<b>a</b>) 500× and (<b>b</b>) 200× magnification.</p> Full article ">Figure 9
<p>Illustration of the three sample tray designs: (<b>a</b>) shallow pocket, (<b>b</b>) deep pocket and (<b>c</b>) deep pocket with an obstacle.</p> Full article ">Figure 10
<p>(<b>a</b>) Measuring area of the samples with a shallow pocket and a deep pocket and (<b>b</b>) measuring area of the sample with a deep pocket and an obstacle.</p> Full article ">Figure 11
<p>Profilometer images of a sample with a shallow pocket (<b>a</b>) before and (<b>b</b>) after electrical smoothing with a vertical distance of 2.0 mm and a travel speed of 50 mm/s.</p> Full article ">Figure 12
<p>Surface roughness S<sub>a</sub> depending on handling and electrical smoothing of the samples.</p> Full article ">Figure 13
<p>Surface roughness S<sub>a</sub> of the samples with a shallow pocket depending on travel speed and vertical distance.</p> Full article ">Figure 14
<p>Height profile of the sample with a shallow pocket (<b>a</b>) before and (<b>b</b>) after electrical smoothing with a vertical distance of 2.0 mm and a travel speed of 35 mm/s.</p> Full article ">Figure 15
<p>(<b>a</b>) Turbulence of powder as a result of electrical smoothing and (<b>b</b>) lack of powder after electrical smoothing.</p> Full article ">Figure 16
<p>Surface roughness S<sub>a</sub> of the samples with a deep pocket depending on travel speed and vertical distance.</p> Full article ">Figure 17
<p>Height profile of the sample with a deep pocket (<b>a</b>) before and (<b>b</b>) after electrical smoothing with a vertical distance of 2.0 mm and a travel speed of 35 mm/s.</p> Full article ">Figure 18
<p>Surface roughness S<sub>a</sub> of the sample with a deep pocket with an obstacle depending on travel speed and vertical distance.</p> Full article ">Figure 19
<p>Height profile of the sample with a deep pocket and an obstacle (<b>a</b>) before and (<b>b</b>) after electrical smoothing with a vertical distance of 2.0 mm and a travel speed of 35 mm/s.</p> Full article ">Figure 20
<p>Height profiles with structured powder bed surfaces of samples (<b>a</b>) with a deep pocket and (<b>b</b>) with a deep pocket and an obstacle after electrical smoothing with a vertical distance of 2.0 mm and a travel speed of 25 mm/s.</p> Full article ">
12 pages, 1160 KiB  
Article
Green Innovation Practices: A Case Study in a Foundry
by Gianluca Fratta, Ivan Stefani, Sara Tapola and Stefano Saetta
J. Manuf. Mater. Process. 2024, 8(3), 111; https://doi.org/10.3390/jmmp8030111 - 26 May 2024
Viewed by 1204
Abstract
The foundry industry is responsible for the production of several potentially polluting and hazardous compounds. One of the major sources of pollution is the use of organic binders for the manufacturing of sand cores and sand moulds. To address this problem, in recent [...] Read more.
The foundry industry is responsible for the production of several potentially polluting and hazardous compounds. One of the major sources of pollution is the use of organic binders for the manufacturing of sand cores and sand moulds. To address this problem, in recent years, the use of low-emission products, known as inorganic binders, has been proposed. Their use in ferrous foundries, otherwise, is limited due to some problematic features that complicate their introduction in the manufacturing process, as often happens when a breakthrough innovation is introduced. In light of this, the aim of this work is to provide a Green Innovation Practice (GIP) to manage the introduction of green breakthrough innovations, as previously described, within an existing productive context. This practice was applied to better manage the experimental phase of the Green Casting Life Project, which aims to evaluate the possibility of using inorganic binders for the production of ferrous castings. After describing the state of the art of GIPs and their application in manufacturing contexts, the paper described the proposed GIP and its application to a real case consisting of testing inorganic binders in a ferrous foundry. Full article
(This article belongs to the Special Issue Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing)
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<p>Two of the most used instruments for stakeholder classification: the Salience model [<a href="#B26-jmmp-08-00111" class="html-bibr">26</a>] (<b>left</b>) and the Power/Interest matrix [<a href="#B27-jmmp-08-00111" class="html-bibr">27</a>] (<b>right</b>).</p> Full article ">Figure 2
<p>Example of flowchart [<a href="#B31-jmmp-08-00111" class="html-bibr">31</a>].</p> Full article ">Figure 3
<p>IDEF0 representation [<a href="#B32-jmmp-08-00111" class="html-bibr">32</a>].</p> Full article ">Figure 4
<p>Deconstruction overview [<a href="#B32-jmmp-08-00111" class="html-bibr">32</a>].</p> Full article ">Figure 5
<p>Essential steps of the manufacturing process.</p> Full article ">Figure 6
<p>The A0 map, realised using IDEF0, includes all the main processes needed for the production of finished cast-iron components.</p> Full article ">Figure 7
<p>The A2 map, realised using IDEF0, includes all the sub-processes needed for the production of cores using inorganic binders.</p> Full article ">
17 pages, 6785 KiB  
Article
Microstructure and Thermal Mechanical Behavior of Arc-Welded Aluminum Alloy 6061-T6
by Zeli Arhumah and Xuan-Tan Pham
J. Manuf. Mater. Process. 2024, 8(3), 110; https://doi.org/10.3390/jmmp8030110 - 26 May 2024
Cited by 1 | Viewed by 887
Abstract
In this study, the welding thermal cycle, as well as the microstructural and mechanical properties of welded AA6061-T6 plates, were studied. The plates were prepared and bead-on-plate welded using gas metal arc welding (GMAW). Numerical simulations using SYSWELD® were performed to obtain [...] Read more.
In this study, the welding thermal cycle, as well as the microstructural and mechanical properties of welded AA6061-T6 plates, were studied. The plates were prepared and bead-on-plate welded using gas metal arc welding (GMAW). Numerical simulations using SYSWELD® were performed to obtain the thermal distribution in the welded plates. The numerical heat source was calibrated using the temperatures obtained from the experimental work and the geometry of the melting pool. The mechanical properties were obtained through microhardness tests and were correlated with the welding thermal cycle. Moreover, the mechanical behavior and local deformation in the heat-affected zone (HAZ) were investigated using micro-flat tensile (MFT) tests with digital image correlation (DIC). The mechanical properties of the subzones in the HAZ were then correlated with the welding thermal cycle and with the microstructure of the HAZ. It was observed that the welding thermal cycle produced microstructural variations across the HAZ, which significantly affected the mechanical behavior of the HAZ subzones. The results revealed that MFT tests with the DIC technique are an excellent tool for studying the local mechanical behavior change in AA6061-T6 welded parts due to the welding heat. Full article
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<p>Location of thermocouples installed on the plate (TC1 to TC8).</p> Full article ">Figure 2
<p>Conical Gaussian volumetric heat source model.</p> Full article ">Figure 3
<p>Mesh used for numerical simulations using Sysweld: (<b>a</b>) application of the heat source, (<b>b</b>) irregular mesh.</p> Full article ">Figure 4
<p>MFT specimen: (<b>a</b>) machined samples, (<b>b</b>,<b>c</b>) extracted micro-tensile specimen (dimensions in mm), and (<b>d</b>) regions of interest extracted from the welded sample used for DIC.</p> Full article ">Figure 4 Cont.
<p>MFT specimen: (<b>a</b>) machined samples, (<b>b</b>,<b>c</b>) extracted micro-tensile specimen (dimensions in mm), and (<b>d</b>) regions of interest extracted from the welded sample used for DIC.</p> Full article ">Figure 5
<p>Melting zone experimental results compared with FEA.</p> Full article ">Figure 6
<p>Thermal profile: (<b>a</b>) comparison between experiments and finite element simulation at points at different distances from the welding center line; (<b>b</b>) peak temperature comparison.</p> Full article ">Figure 7
<p>Base metal of AA6061-T6: (<b>a</b>) coarse second phase particles distributed randomly; (<b>b</b>) grain size of base metal.</p> Full article ">Figure 8
<p>Light optical micrographs of microstructure of FZ and HAZ: (<b>a</b>) grains in FZ; (<b>b</b>) interface between the HAZ and fusion zone FZ.</p> Full article ">Figure 9
<p>Light optical micrographs of recrystallization and grain growth: (<b>a</b>) zone adjacent to FZ; (<b>b</b>) gradual change in grain size in the zone beneath the FZ.</p> Full article ">Figure 10
<p>Correlation between thermal cycle and microhardness values in HAZ.</p> Full article ">Figure 11
<p>Global response (stress–strain) curves for welded sample and parent metal.</p> Full article ">Figure 12
<p>Illustration of the distribution of local in-plane strain acquired using DIC equivalent to different strains (corresponding to 24 mm specimen gauge length) for two specimens.</p> Full article ">Figure 13
<p>Average value of ԑ<sub>XX</sub> and ԑ<sub>YY</sub> in transverse weld direction for three stress/strain stages: (<b>a</b>) 126 MPa_0.5%; (<b>b</b>) 202 MPa_2.4%; and (<b>c</b>) 225 MPa_5.1%, illustrating local in-plane strain localization.</p> Full article ">Figure 14
<p>Different locations studied on the welded sample.</p> Full article ">Figure 15
<p>Stress-local strain in different locations obtained by DIC for welded sample.</p> Full article ">Figure 16
<p>Average stress–strain behavior (DIC measurements) for welded joint and fracture planes in HAZ (PZ1 and PZ2) compared to base metal thermal cycle distribution across gauge length of DIC test specimen.</p> Full article ">Figure 17
<p>Historical temperature distribution (°C) of extracted samples for DIC tests.</p> Full article ">Figure 18
<p>Correlations between mechanical properties and peak temperature profile along FZ and HAZ (Test-1 and Test-2).</p> Full article ">
15 pages, 8105 KiB  
Article
Milling Stability Modeling by Sample Partitioning with Chatter Frequency-Based Test Point Selection
by Tony Schmitz
J. Manuf. Mater. Process. 2024, 8(3), 109; https://doi.org/10.3390/jmmp8030109 - 24 May 2024
Viewed by 1133
Abstract
This paper describes a sample partitioning approach to retain or reject samples from an initial distribution of stability maps using milling test results. The stability maps are calculated using distributions of uncertain modal parameters that represent the tool tip frequency response functions and [...] Read more.
This paper describes a sample partitioning approach to retain or reject samples from an initial distribution of stability maps using milling test results. The stability maps are calculated using distributions of uncertain modal parameters that represent the tool tip frequency response functions and cutting force model coefficients. Test points for sample partitioning are selected using either (1) the combination of spindle speed and mean axial depth from the available samples that provides the high material removal rate, or (2) a spindle speed based on the chatter frequency and mean axial depth at that spindle speed. The latter is selected when an unstable (chatter) result is obtained from a test. Because the stability model input parameters are also partitioned using the test results, their uncertainty is reduced using a limited number of tests and the milling stability model accuracy is increased. A case study is provided to evaluate the algorithm. Full article
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<p>Sample partitioning using stability testing.</p> Full article ">Figure 2
<p>Cutting force model.</p> Full article ">Figure 3
<p>Sample partitioning algorithm description. A red × indicates an unstable result and a green circle indicates a stable result.</p> Full article ">Figure 4
<p>Variation in chatter frequency with spindle speed and portion of the stability boundary that is exceeded. The stability map is shown at the bottom, where <span class="html-italic">b<sub>lim</sub></span> is the limiting axial depth to avoid chatter, and the insets show the frequency-dependent magnitude of the <span class="html-italic">x</span>-direction displacement for the two spindle speeds. The tooth passing frequency and its harmonics are identified by the open red circles.</p> Full article ">Figure 5
<p>Initial distributions of (<b>a</b>) <span class="html-italic">f<sub>n</sub></span><sub>1</sub>; (<b>b</b>) <span class="html-italic">k</span><sub>1</sub>; (<b>c</b>) ζ<sub>1</sub>; (<b>d</b>) <span class="html-italic">f<sub>n</sub></span><sub>2</sub>; (<b>e</b>) <span class="html-italic">k</span><sub>2</sub>; and (<b>f</b>) ζ<sub>2</sub>. The histograms include 50 bins with 1 × 10<sup>4</sup> samples, so there are approximately 200 samples per bin. The magenta lines identify the true values.</p> Full article ">Figure 6
<p>Initial distributions of (<b>a</b>) <span class="html-italic">k<sub>t</sub></span> and (<b>b</b>) <span class="html-italic">k<sub>n</sub></span>. The magenta lines identify the true values.</p> Full article ">Figure 7
<p>Distributions of 10 (<b>a</b>) FRFs and (<b>b</b>) stability maps. The real and imaginary parts of the FRF and the stability map obtained from the true values in <a href="#jmmp-08-00109-t001" class="html-table">Table 1</a> are identified by magenta curves.</p> Full article ">Figure 8
<p>Initial distribution of stability maps (blue curves) and first test point (white square). The stability map obtained from the true model input values is included as the magenta curve.</p> Full article ">Figure 9
<p>Frequency content of the <span class="html-italic">x</span>-direction tool displacement for the {17,547 rpm, 3.5082 mm} test point. The test is unstable and the chatter frequency is 1002.4 Hz.</p> Full article ">Figure 10
<p>Remaining stability maps (5933) after the unstable test at {17,547 rpm, 3.5082 mm}; the test point is marked by the red ×. The second test point is identified by a white square.</p> Full article ">Figure 11
<p>Distribution of 5933 remaining natural frequencies after the first test: (<b>a</b>) <span class="html-italic">f<sub>n</sub></span><sub>1</sub> and (<b>b</b>) <span class="html-italic">f<sub>n</sub></span><sub>2</sub>. The horizontal axis ranges are identical to <a href="#jmmp-08-00109-f005" class="html-fig">Figure 5</a>.</p> Full article ">Figure 12
<p>Mean values of the stability model input parameters as a function of test number (<b>a</b>) natural frequency (<b>b</b>) stiffness (<b>c</b>) damping ratio (<b>d</b>) cutting force coefficients. The true values are indicated by the horizontal dashed lines.</p> Full article ">Figure 13
<p>Sample partitioning results for each test. The remaining maps after each test are shown where the green circle or red × indicates if the test was stable or unstable. The next test point is identified by a square (white or black to provide the best visibility). The numerals 1–14 indicate the test number.</p> Full article ">Figure 14
<p>Point 12 from <a href="#jmmp-08-00109-t002" class="html-table">Table 2</a> is identified by a black square. Although the result is predicted to be unstable using the stability map predicted from the true model inputs (magenta), it was observed to be stable in the time domain simulation, which provided the test result for this study.</p> Full article ">Figure 15
<p>Comparison of results using the true (magenta) and post-partitioning (blue) values.</p> Full article ">
23 pages, 3023 KiB  
Article
Tool Wear Monitoring in Micro-Milling Based on Digital Twin Technology with an Extended Kalman Filter
by Christiand, Gandjar Kiswanto, Ario Sunar Baskoro, Zulhendri Hasymi and Tae Jo Ko
J. Manuf. Mater. Process. 2024, 8(3), 108; https://doi.org/10.3390/jmmp8030108 - 23 May 2024
Viewed by 1041
Abstract
In order to avoid catastrophic events that degrade the quality of machined products, such as tool breakage, it is vital to have a prognostic system for monitoring tool wear during the micro-milling process. Despite the long history of the tool wear monitoring field, [...] Read more.
In order to avoid catastrophic events that degrade the quality of machined products, such as tool breakage, it is vital to have a prognostic system for monitoring tool wear during the micro-milling process. Despite the long history of the tool wear monitoring field, creating such a system to track, monitor, and foresee the rapid progression of tool wear still needs to be improved in the application of micro-milling. On the other hand, digital twin technology has recently become widely recognized as significant in manufacturing and, notably, within the Industry 4.0 ecosystem. Digital twin technology is considered a potential breakthrough in developing a prognostic tool wear monitoring system, as it enables the tracking, monitoring, and prediction of the dynamics of a twinned object, e.g., a CNC machine tool. However, few works have explored the digital twin technology for tool wear monitoring, particularly in the micro-milling field. This paper presents a novel tool wear monitoring system for micro-milling machining based on digital twin technology and an extended Kalman filter framework. The proposed system provides wear progression notifications to assist the user in making decisions related to the machining process. In an evaluation using four machining datasets of slot micro-milling, the proposed system achieved a maximum error mean of 0.038 mm from the actual wear value. The proposed system brings a promising opportunity to widen the utilization of digital twin technology with the extended Kalman filter framework for seamless data integration for wear monitoring service. Full article
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<p>DT model for micro-milling applications.</p> Full article ">Figure 2
<p>The interactions between physical–virtual resources and the pipeline of calculating the micro-milling dynamics.</p> Full article ">Figure 3
<p>Timing diagram of micro-milling machining events showing the profile of DT variables.</p> Full article ">Figure 4
<p>The method of detecting a collision between the tool and the workpiece: (<b>a</b>) FreeCAD module; (<b>b</b>) simple geometry.</p> Full article ">Figure 5
<p>(<b>a</b>) Acquisition and analysis of tool wear images during the machining process; (<b>b</b>) hardness test points; (<b>c</b>) slot machining paths; (<b>d</b>) micro-cutting tool.</p> Full article ">Figure 6
<p>The process variables for the validation step: (<b>a</b>) machining speed <math display="inline"><semantics> <msub> <mi>ω</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> </mrow> </msub> </semantics></math>, (<b>b</b>) zoomed region of A, (<b>c</b>) motor torque <span class="html-italic">T</span>, (<b>d</b>) zoomed region of B, (<b>e</b>) motor current <span class="html-italic">i</span>, and (<b>f</b>) zoomed region of C.</p> Full article ">Figure 7
<p>The performance of EKF-based TWM in all experimental datasets: (<b>a</b>) first dataset, (<b>b</b>) second dataset, (<b>c</b>) third dataset, and (<b>d</b>) fourth dataset; (<b>e</b>) zoomed region of D, (<b>f</b>) zoomed region of E, (<b>g</b>) Kalman gain calculated during EKF-based TWM, and (<b>h</b>) error of the EKF estimate measured with real wear measurements.</p> Full article ">Figure 8
<p>Motor torque based on the wear value: calculated <math display="inline"><semantics> <mfenced separators="" open="(" close=")"> <msub> <mover accent="true"> <mi>T</mi> <mo stretchy="false">^</mo> </mover> <mi>t</mi> </msub> <mspace width="0.277778em"/> <mspace width="0.277778em"/> <mi>vs</mi> <mo>.</mo> <mspace width="0.277778em"/> <mspace width="0.277778em"/> <mover accent="true"> <mi>V</mi> <mo stretchy="false">^</mo> </mover> <mspace width="-0.166667em"/> <msub> <mi>B</mi> <mi>t</mi> </msub> </mfenced> </semantics></math> and actual <math display="inline"><semantics> <mfenced separators="" open="(" close=")"> <msubsup> <mi>T</mi> <mrow> <mi>t</mi> </mrow> <mrow> <mi>R</mi> <mi>e</mi> <mi>a</mi> <mi>l</mi> </mrow> </msubsup> <mspace width="0.277778em"/> <mspace width="0.277778em"/> <mi>vs</mi> <mo>.</mo> <mspace width="0.277778em"/> <mspace width="0.277778em"/> <mi>V</mi> <mspace width="-0.166667em"/> <msubsup> <mi>B</mi> <mrow> <mi>t</mi> </mrow> <mrow> <mi>R</mi> <mi>e</mi> <mi>a</mi> <mi>l</mi> </mrow> </msubsup> </mfenced> </semantics></math>: (<b>a</b>) first dataset, (<b>b</b>) second dataset, (<b>c</b>) third dataset, and (<b>d</b>) fourth dataset.</p> Full article ">Figure 9
<p>Graphical user interface (GUI) of the TWM service with a DT running in parallel.</p> Full article ">
14 pages, 997 KiB  
Article
A Data-Driven Approach for Cutting Force Prediction in FEM Machining Simulations Using Gradient Boosted Machines
by Tim Reeber, Jan Wolf and Hans-Christian Möhring
J. Manuf. Mater. Process. 2024, 8(3), 107; https://doi.org/10.3390/jmmp8030107 - 23 May 2024
Viewed by 1030
Abstract
Cutting simulations via the Finite Element Method (FEM) have recently gained more significance due to ever increasing computational performance and thus better resulting accuracy. However, these simulations are still time consuming and therefore cannot be deployed for an in situ evaluation of the [...] Read more.
Cutting simulations via the Finite Element Method (FEM) have recently gained more significance due to ever increasing computational performance and thus better resulting accuracy. However, these simulations are still time consuming and therefore cannot be deployed for an in situ evaluation of the machining processes in an industrial environment. This is due to the high non-linear nature of FEM simulations of machining processes, which require considerable computational resources. On the other hand, machine learning methods are known to capture complex non-linear behaviors. One of the most widely applied material models in cutting simulations is the Johnson–Cook material model, which has a great influence on the output of the cutting simulations and contributes to the non-linear behavior of the models, but its influence on cutting forces is sometimes difficult to assess beforehand. Therefore, this research aims to capture the highly non-linear behavior of the material model by using a dataset of multiple short-duration cutting simulations from Abaqus to learn the relationship of the Johnson–Cook material model parameters and the resulting cutting forces for a constant set of cutting conditions. The goal is to shorten the time to simulate cutting forces by encapsulating complex cutting conditions in dependence of material parameters in a single model. A total of five different models are trained and the performance is evaluated. The results show that Gradient Boosted Machines capture the influence of varying material model parameters the best and enable good predictions of cutting forces as well as deliver insights into the relevance of the material parameters for the cutting and thrust forces in orthogonal cutting. Full article
(This article belongs to the Special Issue Advances in Metal Cutting and Cutting Tools)
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<p>Exemplary results of von Mises flow stress at a total time of 0.0015 s (end of one simulation).</p> Full article ">Figure 2
<p>Boundary conditions and dimensions of the simulation.</p> Full article ">Figure 3
<p>FFNN topology.</p> Full article ">Figure 4
<p>Simulated cutting force Fc and simulated thrust force Ft for constant cutting conditions. Each data point represents a different J-C material model.</p> Full article ">Figure 5
<p>Simulated results for F<sub>c</sub> and F<sub>t</sub> and the predicted results from the two models for the test dataset. Outliers in red correspond to −40% or 80% values of either or both <span class="html-italic">B</span> and <span class="html-italic">m</span>.</p> Full article ">Figure 6
<p>Feature importance for the final LightGBM model: importance of the J-C parameters for the individual models and therefore the cutting and thrust forces.</p> Full article ">Figure 7
<p>Comparison between simulated and predicted cutting and thrust forces for a parameter set outside the training and test datasets for Ti6Al4V.</p> Full article ">
30 pages, 17895 KiB  
Article
Optimization of the FDM Processing Parameters on the Compressive Properties of ABS Objects for the Production of High-Heeled Shoes
by Suzana Kutnjak-Mravlinčić, Damir Godec, Ana Pilipović and Ana Sutlović
J. Manuf. Mater. Process. 2024, 8(3), 106; https://doi.org/10.3390/jmmp8030106 - 22 May 2024
Viewed by 730
Abstract
The influence of 3D printing parameters on compressive properties is an important factor in the application of additive manufacturing processes for products subjected to compressive loads in use. In this study, the compressive strength and compressive modulus of acrylonitrile/butadiene/styrene (ABS) test specimens fabricated [...] Read more.
The influence of 3D printing parameters on compressive properties is an important factor in the application of additive manufacturing processes for products subjected to compressive loads in use. In this study, the compressive strength and compressive modulus of acrylonitrile/butadiene/styrene (ABS) test specimens fabricated using the fused deposition modeling (FDM) process were investigated with the aim of producing products of high-heeled shoes for women. The experimental part of the study includes a central composite experimental design to optimize the main 3D printing parameters (layer thickness, infill density, and extrusion temperature) and the infill geometry (honeycomb and linear at a 45° angle—L45) to achieve maximum printing properties of the 3D-printed products. The results show that the infill density has the greatest influence on the printing properties, followed by the layer thickness and, finally, the extrusion temperature as the least influential factor. The linear infill at a 45° angle resulted in higher compressive strength and lower compressive modulus values compared to the honeycomb infill. By optimizing the results, the maximum compressive strength (that of L45 is 41 N/mm2 and that of honeycomb 35 N/mm2) and modulus (that of L45 is 918 N/mm2 and that of honeycomb is 868 N/mm2) for both types of infill is obtained at a layer thickness of 0.1 mm and infill density of 40%, while the temperature for L45 can be in the range of 209 °C to 254 °C, but for the honeycomb infill, the processing temperature is 255 °C. Additionally, the study highlights the potential for sustainable manufacturing practices and the integration of advanced 3D printing technologies to enhance the efficiency and eco-friendliness of the production process. Full article
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<p>Production of the test specimen: (<b>a</b>) representation of the forces acting on a high-heeled women’s shoe and CAD model of the heel; (<b>b</b>) layer alignment of the specimen during 3D printing and testing of the compressive modulus and compressive strength.</p> Full article ">Figure 2
<p>Production of the test specimens: (<b>a</b>) positioning on a 3D printer base; (<b>b</b>) 3D printing on a MakerBot Replicator 2X desktop printer; (<b>c</b>) test specimens produced for testing the compressive modulus (<b>d</b>) and compressive strength.</p> Full article ">Figure 3
<p>Testing of the compressive properties: (<b>a</b>) compressive strength; (<b>b</b>) compressive modulus.</p> Full article ">Figure 4
<p>Compressive stress–strain diagram for determining the compressive strength of the linear infill (L45).</p> Full article ">Figure 5
<p>Compressive stress–strain diagram for determining the compressive strength of the honeycomb infill (H).</p> Full article ">Figure 6
<p>Compressive stress–strain diagram for determining the compressive modulus of the linear infill (L45).</p> Full article ">Figure 7
<p>Compressive stress–strain diagram for determining the compressive modulus of the honeycomb infill (H).</p> Full article ">Figure 8
<p>Two-dimensional (2D) graph of the influence of layer thickness on the compressive strength of the linear infill (infill density: 40%, extrusion temperature: 255 °C).</p> Full article ">Figure 9
<p>Two-dimensional (2D) graph of the influence of the infill density on the compressive strength of the linear infill (layer thickness: 0.10 mm, extrusion temperature: 255 °C).</p> Full article ">Figure 10
<p>A 3D graph of the simultaneous influence of the infill density and layer thickness on the compressive strength of the linear infill (extrusion temperature of 255 °C).</p> Full article ">Figure 11
<p>Two-dimensional (2D) graph of the influence of the layer thickness on the compressive strength of the honeycomb infill (infill density: 40%, extrusion temperature: 255 °C).</p> Full article ">Figure 12
<p>Two-dimensional (2D) graph of the influence of the infill density on the compressive strength of the honeycomb infill (layer thickness: 0.10 mm, extrusion temperature: 255 °C).</p> Full article ">Figure 13
<p>Two-dimensional (2D) graph of the influence of the extrusion temperature on the compressive strength of the honeycomb infill (layer thickness: 0.10 mm, infill density: 40%).</p> Full article ">Figure 14
<p>A 3D graph of the simultaneous influence of the infill density and layer thickness on the compressive strength of the honeycomb infill (extrusion temperature of 205 °C).</p> Full article ">Figure 15
<p>A 3D graph of the simultaneous influence of the infill density and layer thickness on the compressive strength of the honeycomb infill (extrusion temperature of 255 °C).</p> Full article ">Figure 16
<p>Two-dimensional (2D) graph of the influence of the layer thickness on the compressive modulus of the linear infill (infill density: 40%, extrusion temperature: 255 °C).</p> Full article ">Figure 17
<p>Two-dimensional (2D) graph of the influence of the infill density on the compressive modulus of the linear infill (layer thickness: 0.10 mm, extrusion temperature: 255 °C).</p> Full article ">Figure 18
<p>A 3D graph of the simultaneous influence of the infill density and layer thickness on the compressive modulus of the linear infill (extrusion temperature of 255 °C).</p> Full article ">Figure 19
<p>Two-dimensional (2D) graph of the influence of the layer thickness on the compressive modulus of the honeycomb infill (infill density: 40%, extrusion temperature: 255 °C).</p> Full article ">Figure 20
<p>Two-dimensional (2D) graph of the influence of the infill density on the compressive modulus of the honeycomb infill (layer thickness: 0.10 mm, extrusion temperature: 255 °C).</p> Full article ">Figure 21
<p>Two-dimensional (2D) graph of the influence of the extrusion temperature on the compressive modulus of the honeycomb infill (layer thickness: 0.10 mm, infill density: 40%).</p> Full article ">Figure 22
<p>A 3D graph of the simultaneous influence of the infill density and layer thickness on the compressive modulus of the honeycomb infill (extrusion temperature of 205 °C).</p> Full article ">Figure 23
<p>A 3D graph of the simultaneous influence of the infill density and layer thickness on the compressive modulus of the honeycomb infill (extrusion temperature of 255 °C).</p> Full article ">Figure 24
<p>CAD models of prototype heels: (<b>a</b>) prototype 1; (<b>b</b>) prototype 2; (<b>c</b>) prototype 3; (<b>d</b>) prototype 4.</p> Full article ">Figure 25
<p>Realized prototypes of functional women’s shoes: (<b>a</b>) prototype 1; (<b>b</b>) prototype 2; (<b>c</b>) prototype 3; (<b>d</b>) prototype 4.</p> Full article ">Figure 25 Cont.
<p>Realized prototypes of functional women’s shoes: (<b>a</b>) prototype 1; (<b>b</b>) prototype 2; (<b>c</b>) prototype 3; (<b>d</b>) prototype 4.</p> Full article ">
13 pages, 3787 KiB  
Article
Experimental Evidence on Incremental Formed Polymer Sheets Using a Stair Toolpath Strategy
by Antonio Formisano, Luca Boccarusso, Dario De Fazio and Massimo Durante
J. Manuf. Mater. Process. 2024, 8(3), 105; https://doi.org/10.3390/jmmp8030105 - 22 May 2024
Viewed by 828
Abstract
Incremental sheet forming represents a relatively recent technology, similar to the layered manufacturing principle of the rapid prototype approach; it is very suitable for small series production and guarantees cost-effectiveness because it does not require dedicated equipment. Research has initially shown that this [...] Read more.
Incremental sheet forming represents a relatively recent technology, similar to the layered manufacturing principle of the rapid prototype approach; it is very suitable for small series production and guarantees cost-effectiveness because it does not require dedicated equipment. Research has initially shown that this process is effective in metal materials capable of withstanding plastic deformation but, in recent years, the interest in this technique has been increasing for the manufacture of complex polymer sheet components as an alternative to the conventional technologies, based on heating–shaping–cooling manufacturing routes. Conversely, incrementally formed polymer sheets can suffer from some peculiar defects, like, for example, twisting. To reduce the risk of this phenomenon, the occurrence of failures and poor surface quality, a viable way is to choose toolpath strategies that make the tool/sheet contact conditions less severe; this represents one of the main goals of the present research. Polycarbonate sheets were worked using incremental forming; in detail, cone frusta with a fixed-wall angle were manufactured with different toolpaths based on a reference and a stair strategy, in lubricated and dry conditions. The forming forces, the forming time, the twist angle, and the mean roughness were monitored. The analysis of the results highlighted that a stair toolpath involving an alternation of diagonal up and vertical down steps represents a useful strategy to mitigate the occurrence of the twisting phenomenon in incremental formed thermoplastic sheets and a viable way of improving the process towards a green manufacturing process. Full article
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<p>Geometrical features of the fixed-wall angle cone frusta and of the forming tool.</p> Full article ">Figure 2
<p>Experimental setup during an incremental forming test.</p> Full article ">Figure 3
<p>Not-to-scale schematisation of the toolpath strategies: (<b>a</b>) conical helix; (<b>b</b>) XY-plane and (<b>c</b>) YZ-plane views of the toolpaths.</p> Full article ">Figure 4
<p>CAD representation of the evaluation of the twist angle.</p> Full article ">Figure 5
<p>Cone frustum manufactured using: (<b>a</b>) the <span class="html-italic">ref_tp</span>; (<b>b</b>) the <span class="html-italic">hr1.5_tp</span>.</p> Full article ">Figure 6
<p>Complete trend of the forming forces using: (<b>a</b>) the <span class="html-italic">ref_tp</span>; (<b>b</b>) the <span class="html-italic">hr1.5_tp</span>.</p> Full article ">Figure 7
<p>Graph of 70 ÷ 80 percent time range trend of the forming forces using: (<b>a</b>) the <span class="html-italic">ref_tp</span>; (<b>b</b>) the <span class="html-italic">hr1.5_tp</span>.</p> Full article ">Figure 8
<p>Forming force data analyses from the last two complete turns of the different toolpaths: (<b>a</b>) boxplot for the vertical component; (<b>b</b>) the average of the vertical component and of the absolute values of the horizontal component.</p> Full article ">
23 pages, 5086 KiB  
Article
Real-Size Reconstruction of Porous Media Using the Example of Fused Filament Fabrication 3D-Printed Rock Analogues
by Alexander A. Oskolkov, Alexander A. Kochnev, Sergey N. Krivoshchekov and Yan V. Savitsky
J. Manuf. Mater. Process. 2024, 8(3), 104; https://doi.org/10.3390/jmmp8030104 - 17 May 2024
Viewed by 1396
Abstract
The multi-scale study of rock properties is a necessary step in the planning of oil and gas reservoir developments. The amount of core samples available for research is usually limited, and some of the samples can be distracted. The investigation of core reconstruction [...] Read more.
The multi-scale study of rock properties is a necessary step in the planning of oil and gas reservoir developments. The amount of core samples available for research is usually limited, and some of the samples can be distracted. The investigation of core reconstruction possibilities is an important task. An approach to the real-size reconstruction of porous media with a given (target) porosity and permeability by controlling the parameters of FFF 3D printing using CT images of the original core is proposed. Real-size synthetic core specimens based on CT images were manufactured using FFF 3D printing. The possibility of reconstructing the reservoir properties of a sandstone core sample was proven. The results of gas porometry measurements showed that the porosity of specimens No.32 and No.46 was 13.5% and 12.8%, and the permeability was 442.3 mD and 337.8 mD, respectively. The porosity of the original core was 14% and permeability was 271 mD. It was found that changing the layer height and nozzle diameter, as well as the retract and restart distances, has a direct effect on the porosity and permeability of synthetic specimens. This study shows that porosity and permeability of synthetic specimens depend on the flow of the material and the percentage of overlap between the infill and the outer wall. Full article
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<p>Tomogram of sandstone core: (<b>a</b>) section along XY axis; (<b>b</b>) section along XZ axis; (<b>c</b>) section along YZ axis; (<b>d</b>) 3D view of void space.</p> Full article ">Figure 2
<p>External appearance of the core (sandstone No.10) and synthetic specimens.</p> Full article ">Figure 3
<p>Results of computed tomography of sectioned specimen No.6: (<b>a</b>) section in the XY plane (z = 477 mm); (<b>b</b>) section in the XZ plane (y = 248 mm).</p> Full article ">Figure 4
<p>External appearance of the core (sandstone No.10) and specimen No.7 prepared in Simplify3d slicer: (<b>a</b>) specimen No.7, side view; (<b>b</b>) core, side view; (<b>c</b>) specimen No.7, top view; (<b>d</b>) core, top view.</p> Full article ">Figure 4 Cont.
<p>External appearance of the core (sandstone No.10) and specimen No.7 prepared in Simplify3d slicer: (<b>a</b>) specimen No.7, side view; (<b>b</b>) core, side view; (<b>c</b>) specimen No.7, top view; (<b>d</b>) core, top view.</p> Full article ">Figure 5
<p>Results of computed tomography of sectioned specimen No.4: (<b>a</b>) section in the XY plane (z = 477 mm); (<b>b</b>) section in the XZ plane (y = 245 mm).</p> Full article ">Figure 6
<p>CT results for specimen No.9 and core in section: (<b>a</b>) section of specimen No.9 in the XZ plane (y = 246 mm); (<b>b</b>) core section in the XZ plane (y = 251 mm).</p> Full article ">Figure 7
<p>Plots of dependence of the porosity and permeability of the specimens on the FFF 3D printing parameters: (<b>a</b>) dependence of porosity on material flow and percentage of overlap; (<b>b</b>) dependence of permeability on material flow and percentage of overlap.</p> Full article ">Figure 7 Cont.
<p>Plots of dependence of the porosity and permeability of the specimens on the FFF 3D printing parameters: (<b>a</b>) dependence of porosity on material flow and percentage of overlap; (<b>b</b>) dependence of permeability on material flow and percentage of overlap.</p> Full article ">
16 pages, 4417 KiB  
Article
Revealing the Mechanisms of Smoke during Electron Beam–Powder Bed Fusion by High-Speed Synchrotron Radiography
by Jihui Ye, Nick Semjatov, Pidassa Bidola, Greta Lindwall and Carolin Körner
J. Manuf. Mater. Process. 2024, 8(3), 103; https://doi.org/10.3390/jmmp8030103 - 17 May 2024
Cited by 1 | Viewed by 1443
Abstract
Electron beam–powder bed fusion (PBF-EB) is an additive manufacturing process that utilizes an electron beam as the heat source to enable material fusion. However, the use of a charge-carrying heat source can sometimes result in sudden powder explosions, usually referred to as “Smoke”, [...] Read more.
Electron beam–powder bed fusion (PBF-EB) is an additive manufacturing process that utilizes an electron beam as the heat source to enable material fusion. However, the use of a charge-carrying heat source can sometimes result in sudden powder explosions, usually referred to as “Smoke”, which can lead to process instability or termination. This experimental study investigated the initiation and propagation of Smoke using in situ high-speed synchrotron radiography. The results reveal two key mechanisms for Smoke evolution. In the first step, the beam–powder bed interaction creates electrically isolated particles in the atmosphere. Subsequently, these isolated particles get charged either by direct irradiation by the beam or indirectly by back-scattered electrons. These particles are accelerated by electric repulsion, and new particles in the atmosphere are produced when they impinge on the powder bed. This is the onset of the avalanche process known as Smoke. Based on this understanding, the dependence of Smoke on process parameters such as beam returning time, beam diameter, etc., can be rationalized. Full article
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<p>Freemelt<sup>®</sup> ONE machine setup. The e-beam ramped up on a ramping plate before impacting the target powder bed. A high-speed camera (Sony rx100 v) was mounted next to the viewport to monitor the Smoke. A custom-made start plate ensured consistent Smoke triggering on a loose powder bed at a specific powder height. Details about the e-beam diameter and the start plate are provided on the right side of the image.</p> Full article ">Figure 2
<p>MiniMelt machine setup. The machine has two observation windows made out of Kapton foil to maintain vacuum atmosphere and provide an X-ray in- and outlet. A powder hill, created by a customized rake, serves as an illumination object. The X-rays transmitted through the powder hill are captured by the radiography system provided by Helmholtz-Zentrum Hereon, consisting of GaGG+ scintillator and Phantom v2640 high-speed camera.</p> Full article ">Figure 3
<p>Smoke experiment with Ti6Al4V. Bright spots observed prior to the Smoke indicate the rapid powder temperature increasing at the impact spot. (<b>a</b>) Smoke experiment initiated with a beam current of 2 mA; spatters form, accompanied by an expanding powder cloud, as indicated by the red circle. (<b>b</b>) Smoke experiment initiated with a beam current of 0.5 mA; a similar pattern of Smoke development is observed.</p> Full article ">Figure 4
<p>Radiography of Smoke development: (<b>a</b>) left, illustration of imaging method; middle, dimension information about the powder hill; right, initial state of the powder hill. (<b>b</b>) Stage-wise Smoke development: melt pool formation (e-beam creates a melt pool), powder spattering (overheating of the melt pool and powder ejected from the surface), and Smoke avalanche (powder lifts at a significantly higher speed).</p> Full article ">Figure 5
<p>Particle velocity analysis during the powder spattering. The red circles mark the moving particles, and the yellow lines indicate their trajectories. The direction of vapor pressure is depicted by green arrows. Particle A is a molten particle with a radius of approximately 120 µm, while Particles B and C are unmolten powder particles.</p> Full article ">Figure 6
<p>Powder behavior during transition phase. Five consecutive frames from transition phase are presented, each processed using two different methods for enhanced analysis: contrast enhancement (<b>upper row</b>) and image subtraction (<b>lower row</b>). Upper row: This row of images visualizes the state of the powder bed. It highlights the movement of four particles repelling each other, color marked. This behavior is presumed to be caused by electrostatic force. Lower row: This row of images demonstrates the dynamics of powder particles. The black-and-white lines represent the trajectories of rapid moving particles. Their quantity visibly escalates over time.</p> Full article ">Figure 7
<p>Analytical model (Equations (2)–(5)) to estimate powder particle repulsion and the resulting velocity as a function of time upon direct e-beam impact. The schematic of melt pool is exaggerated for the illustration of metal evaporation.</p> Full article ">Figure 8
<p>Quasi-analytical simulation of powder movement due to BSE charging. (<b>a</b>) Left: Schematic illustrating geometrical parameters for the calculation of the backscattered electrons in the solid angle, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">Ω</mi> </mrow> <mrow> <mi>P</mi> </mrow> </msub> </mrow> </semantics></math>. Right: Fraction of backscattered electrons as a function of the emission angle and the distance, <math display="inline"><semantics> <mrow> <mi>R</mi> </mrow> </semantics></math>, to the primary interaction zone. (<b>b</b>) Left: Schematic representation of powder particles charged by BSE. Right: The calculated velocity of the particles using the parameters listed in <a href="#jmmp-08-00103-t001" class="html-table">Table 1</a>. The charging rate is set to a constant, with <math display="inline"><semantics> <mrow> <mfrac> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>B</mi> </mrow> </msub> </mrow> <mrow> <mi>R</mi> </mrow> </mfrac> </mrow> </semantics></math> being 5 × 10<sup>−5</sup>.</p> Full article ">Figure 9
<p>A sequential illustration of Smoke development under a stationary e-beam, highlighting key mechanisms. (<b>a</b>) E-beam activation: Upon the e-beam activation, the top layer of powder particles rapidly heats up. Electron dissipation rates are sufficiently high to prevent charge accumulation. (<b>b</b>) Melt pool formation: The continuous e-beam irradiation leads to the melting of the powder bed, and electron dissipation remains adequate. (<b>c</b>) Powder bed disturbance and charge accumulation: Further e-beam irradiation overheats the melt pool, causing two primary effects: disturbances in the powder bed from metal vapor, creating electrically isolated particles, and backscattering of the e-beam from the melt pool surface. (<b>d</b>) Powder repulsion: Accumulated charge on airborne particles causes repulsion through electrostatic forces. (<b>e</b>) Powder bombardment: Electrostatic forces propel charged particles in various directions, some of which impact the powder bed like ‘bombs’, further isolating particles and transferring charges to neighboring particles. (<b>f</b>) Avalanche: The escalation of processes from steps (<b>c</b>) to (<b>e</b>) occurs, leading some particles to enter the e-beam region and triggering a rapid avalanche. The previous model highlighted two key mechanisms that contribute to the Smoke development: the generation of disturbances in the powder bed, resulting in the formation of electrically isolated particles, and the subsequent electrons accumulation on these particles. Disturbances arise from various PBF-EB process-related events, such as the metal vapor from the melt pool, thermal expansion of powder particles, the melt pool’s wetting behavior, etc. The second key mechanism is the accumulation of electrons on these isolated particles, whether through electron scattering or even direct impact.</p> Full article ">
20 pages, 22048 KiB  
Article
Digital Twin Modeling for Smart Injection Molding
by Sara Nasiri, Mohammad Reza Khosravani, Tamara Reinicke and Jivka Ovtcharova
J. Manuf. Mater. Process. 2024, 8(3), 102; https://doi.org/10.3390/jmmp8030102 - 17 May 2024
Cited by 1 | Viewed by 1695
Abstract
In traditional injection molding, each level of the process has its own monitoring and improvement initiatives. But in the upcoming industrial revolution, it is important to establish connections and communication among all stages, as changes in one stage might have an impact on [...] Read more.
In traditional injection molding, each level of the process has its own monitoring and improvement initiatives. But in the upcoming industrial revolution, it is important to establish connections and communication among all stages, as changes in one stage might have an impact on others. To address this issue, digital twins (DTs) are introduced as virtual models that replicate the entire injection molding process. This paper focuses on the data and technology needed to build a DT model for injection molding. Each stage can have its own DT, which are integrated into a comprehensive model of the process. DTs enable the smart automation of production processes and data collection, reducing manual efforts in supervising and controlling production systems. However, implementing DTs is challenging and requires effort for conception and integration with the represented systems. To mitigate this, the current work presents a model for systematic knowledge-based engineering for the DTs of injection molding. This model includes fault detection systems, 3D printing, and system integration to automate development activities. Based on knowledge engineering, data analysis, and data mapping, the proposed DT model allows fault detection, prognostic maintenance, and predictive manufacturing. Full article
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<p>A schematic of injection molding machine and a molding production cycle.</p> Full article ">Figure 2
<p>Main pillars of Industry 4.0 in the production process.</p> Full article ">Figure 3
<p>Conceptual diagram of the proposed DT framework.</p> Full article ">Figure 4
<p>Knowledge-based framework of DT model for smart injection molding.</p> Full article ">Figure 5
<p>Schematic overview of the proposed DT modeling.</p> Full article ">Figure 6
<p>Identified features of process parameters in injection molding machine.</p> Full article ">Figure 7
<p>Mold of the drippers (<b>left</b>), faulty and perfect fabricated drippers (<b>right</b>).</p> Full article ">Figure 8
<p>Fabricated 3D-printed mold for production of the chip key.</p> Full article ">Figure 9
<p>Design parameter of 3D printing [<a href="#B23-jmmp-08-00102" class="html-bibr">23</a>].</p> Full article ">Figure 10
<p>The flowchart of the proposed system.</p> Full article ">Figure 11
<p>Injection molding and extrusion machines in Semnan Polyethylene Pipe and Fitting Co.</p> Full article ">Figure 12
<p>Two-axis charts for comparing time and cost in traditional process and smart solutions.</p> Full article ">
37 pages, 9937 KiB  
Article
A Study on Powder Spreading Quality in Powder Bed Fusion Processes Using Discrete Element Method Simulation
by Panagiotis Avrampos and George-Christopher Vosniakos
J. Manuf. Mater. Process. 2024, 8(3), 101; https://doi.org/10.3390/jmmp8030101 - 16 May 2024
Cited by 1 | Viewed by 1011
Abstract
Powder deposition is a very important aspect of PBF-based additive manufacturing processes. Discrete Element Method (DEM) is commonly utilized by researchers to examine the physically complex aspects of powder-spreading methods. This work focuses on vibration-assisted doctor blade powder recoating. The aim of this [...] Read more.
Powder deposition is a very important aspect of PBF-based additive manufacturing processes. Discrete Element Method (DEM) is commonly utilized by researchers to examine the physically complex aspects of powder-spreading methods. This work focuses on vibration-assisted doctor blade powder recoating. The aim of this work is to use experiment-verified DEM simulations in combination with Taguchi Design of Experiments (DoE) to identify optimum spreading parameters based on robust layer quality criteria. The verification of the used powder model is performed via angle of repose and angle of avalanche simulation–experiment cross-checking. Then, four criteria, namely layer thickness deviation, surface coverage ratio, surface root-mean-square roughness and true packing density, are defined. It has been proven that the doctor blade’s translational speed plays the most important role in defining the quality of the deposited layer. The true packing density was found to be unaffected by the spreading parameters. The vertical vibration of the doctor blade recoater was found to have a beneficial effect on the quality of the deposited layer. Ultimately, a weighted mean quality criteria analysis is mapped out. Skewness and kurtosis were proven to function as effective indicators of layer quality, showing a linear relation to the weighted means of the defined quality criteria. The specific weights that optimize this linearity were identified. Full article
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<p>Visual depiction of the work of adhesion between the two surfaces and order of contact evolution [<a href="#B28-jmmp-08-00101" class="html-bibr">28</a>].</p> Full article ">Figure 2
<p>Surface area vs. particle diameter plot for spherical a-alumina particles.</p> Full article ">Figure 3
<p>Tabor parameter curve vs. the second particle’s diameter for two extreme diameters of the first particle.</p> Full article ">Figure 4
<p>(<b>a</b>) The physical bodies of the powder spreading simulation; (<b>b</b>) semi-transparent cyan cube, which functions as a powder factory.</p> Full article ">Figure 5
<p>(<b>Left</b>) The initial moment of powder generation; (<b>Middle</b>) particles’ free-fall onto the substrate, over the sample square; (<b>Right</b>) particles’ free-fall complete. The slope has been created/deposition is ready to begin.</p> Full article ">Figure 6
<p>Sample top view of the deposited powder layer, with the coloring of the particles via (<b>a</b>) vertical position and (<b>b</b>) diameter.</p> Full article ">Figure 7
<p>Flowchart of the top-surface layer calculation code.</p> Full article ">Figure 8
<p>Black circles are uncovered areas where the substrate is visible, denoting coverage defects.</p> Full article ">Figure 9
<p>Infinitesimal prism visualization.</p> Full article ">Figure 10
<p>Powder deposition example (top and side views) with a non-vibrating doctor blade at deposition speed of: (<b>a</b>) 0.08 m/s; (<b>b</b>) 0.01 m/s.</p> Full article ">Figure 11
<p>Side view of the doctor blade. Depiction of the angle of relief and the blade’s profile.</p> Full article ">Figure 12
<p>Means of the quality criteria: (<b>a</b>) LTD; (<b>b</b>) SCR; (<b>c</b>) RMS; (<b>d</b>) PD.</p> Full article ">Figure 13
<p>Signal-to-noise ratios: (<b>a</b>) LTD; (<b>b</b>) SCR; (<b>c</b>) RMS; (<b>d</b>) PD.</p> Full article ">Figure 14
<p>Interaction plots for means of |LTD|: (<b>a</b>) u<sub>tr</sub>-f<sub>vib</sub>; (<b>b</b>) u<sub>tr</sub>-A<sub>vib</sub>; (<b>c</b>) u<sub>tr</sub>-θ<sub>rel</sub>.</p> Full article ">Figure 15
<p>Interaction plots for means of SCR: (<b>a</b>) u<sub>tr</sub>-f<sub>vib</sub>; (<b>b</b>) u<sub>tr</sub>-A<sub>vib</sub>; (<b>c</b>) u<sub>tr</sub>-θ<sub>rel</sub>.</p> Full article ">Figure 16
<p>Interaction plots for means of S<sub>q</sub>-RMS: (<b>a</b>) u<sub>tr</sub>-f<sub>vib</sub>; (<b>b</b>) u<sub>tr</sub>-A<sub>vib</sub>; (<b>c</b>) u<sub>tr</sub>-θ<sub>rel</sub>.</p> Full article ">Figure 17
<p>Quality criteria vs. Ssk.</p> Full article ">Figure 18
<p>Quality criteria vs. Sku.</p> Full article ">Figure 19
<p>Best-fit line WM<sub>Ssk</sub> versus Ssk.</p> Full article ">Figure 20
<p>Best-fit line WM<sub>Sku</sub> versus Sku.</p> Full article ">Figure 21
<p>Layer quality comparison: (<b>a</b>) vibration-less (top and side); (<b>b</b>) vibration-assisted optimum (top and side). (Note: Magenta: back border plate; pink: front border plate).</p> Full article ">
20 pages, 807 KiB  
Article
Faster Evaluation of Dimensional Machine Performance in Additive Manufacturing by Using COMPAQT Parts
by Laurent Spitaels, Endika Nieto Fuentes, Valentin Dambly, Edouard Rivière-Lorphèvre, Pedro-José Arrazola and François Ducobu
J. Manuf. Mater. Process. 2024, 8(3), 100; https://doi.org/10.3390/jmmp8030100 - 16 May 2024
Viewed by 835
Abstract
Knowing the tolerance interval capabilities (TICs) of a manufacturing process is of prime interest, especially if specifications link the manufacturer to a customer. These TICs can be determined using the machine performance concept of ISO 22514. However, few works have applied this to [...] Read more.
Knowing the tolerance interval capabilities (TICs) of a manufacturing process is of prime interest, especially if specifications link the manufacturer to a customer. These TICs can be determined using the machine performance concept of ISO 22514. However, few works have applied this to Additive Manufacturing printers, while testing most of the printing area as recommended takes a very long time (nearly 1 month is common). This paper, by proposing a novel part design called COMPAQT (Component for Machine Performances Assessment in Quick Time), aims at giving the same level of printing area coverage, while keeping the manufacturing time below 24 h. The method was successfully tested on a material extrusion printer. It allowed the determination of potential and real machine tolerance interval capabilities. Independently of the feature size, those aligned with the X axis achieved lower TICs than those aligned with the Y axis, while the Z axis exhibited the best performance. The measurements specific to one part exhibited a systematic error centered around 0 mm ± 0.050 mm, while those involving two parts reached up to 0.314 mm of deviation. COMPAQT can be used in two applications: evaluating printer tolerance interval capabilities and tracking its long-term performance by incorporating it into batches of other parts. Full article
(This article belongs to the Special Issue Design, Processes and Materials for Additive Manufacturing)
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<p>Design of the COMPAQT part with maximal dimensions (<b>a</b>) and axis of symmetry (<b>b</b>).</p> Full article ">Figure 2
<p>Distribution of the 25 COMPAQT parts on the build platform (in green) within the available printing area (red square).</p> Full article ">Figure 3
<p>Batch of 25 COMPAQT parts after their manufacturing in an Ultimaker 2+ printer.</p> Full article ">Figure 4
<p>Measurement configuration used with the CMM.</p> Full article ">Figure 5
<p>Example of fitting with a gamma distribution of the 150 measurements across the Y axis belonging to the 50–80 mm class.</p> Full article ">Figure 6
<p>Best-fitting distributions for the Part Specific measurements (belonging to size ranges from 1–3 mm to 10–18 mm for the X, Y, and Z axes).</p> Full article ">Figure 7
<p>Best-fitting distributions for the Multi Part measurements (belonging to size ranges from 18–30 mm to 120–180 mm for the X, Y, and Z axes).</p> Full article ">Figure 8
<p>Potential tolerance interval capabilities in mm depending on the dimensional size ranges of ISO 286-1 considering a <math display="inline"><semantics> <msub> <mi>P</mi> <mi>m</mi> </msub> </semantics></math> target of 1.67 at a confidence level of 95%.</p> Full article ">Figure 9
<p>Potential tolerance interval capabilities in mm depending on the dimensional size ranges of ISO 286-1 considering a <math display="inline"><semantics> <msub> <mi>P</mi> <mi>m</mi> </msub> </semantics></math> target of 1 at a confidence level of 95%.</p> Full article ">Figure 10
<p>Average relative deviations in % depending on the X, Y, and Z axes and global indicator, blue is used for negative relative deviations, red for the positive, and white for the values approaching zero.</p> Full article ">
14 pages, 4717 KiB  
Article
Exploring Multi-Armed Bandit (MAB) as an AI Tool for Optimising GMA-WAAM Path Planning
by Rafael Pereira Ferreira, Emil Schubert and Américo Scotti
J. Manuf. Mater. Process. 2024, 8(3), 99; https://doi.org/10.3390/jmmp8030099 - 15 May 2024
Viewed by 1116
Abstract
Conventional path-planning strategies for GMA-WAAM may encounter challenges related to geometrical features when printing complex-shaped builds. One alternative to mitigate geometry-related flaws is to use algorithms that optimise trajectory choices—for instance, using heuristics to find the most efficient trajectory. The algorithm can assess [...] Read more.
Conventional path-planning strategies for GMA-WAAM may encounter challenges related to geometrical features when printing complex-shaped builds. One alternative to mitigate geometry-related flaws is to use algorithms that optimise trajectory choices—for instance, using heuristics to find the most efficient trajectory. The algorithm can assess several trajectory strategies, such as contour, zigzag, raster, and even space-filling, to search for the best strategy according to the case. However, handling complex geometries by this means poses computational efficiency concerns. This research aimed to explore the potential of machine learning techniques as a solution to increase the computational efficiency of such algorithms. First, reinforcement learning (RL) concepts are introduced and compared with supervised machining learning concepts. The Multi-Armed Bandit (MAB) problem is explained and justified as a choice within the RL techniques. As a case study, a space-filling strategy was chosen to have this machining learning optimisation artifice in its algorithm for GMA-AM printing. Computational and experimental validations were conducted, demonstrating that adding MAB in the algorithm helped to achieve shorter trajectories, using fewer iterations than the original algorithm, potentially reducing printing time. These findings position the RL techniques, particularly MAB, as a promising machining learning solution to address setbacks in the space-filling strategy applied. Full article
(This article belongs to the Special Issue Advances in Directed Energy Deposition Additive Manufacturing)
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<p>A classical symbolic representation of the Multi-Armed Bandit problem.</p> Full article ">Figure 2
<p>Simplified algorithm flowchart of the original space-filling-based strategy.</p> Full article ">Figure 3
<p>Simplified algorithm flowchart of the MAB-problem-based strategy.</p> Full article ">Figure 4
<p>3D model of a complex shape (a logo) used to validate the use of the MAB problem approach in the trajectory-planning algorithm (nominal dimension of 160 mm diameter and 22 mm high).</p> Full article ">Figure 5
<p>Convergence (<b>a</b>) and Cumulative Regret (<b>b</b>) analysis charts of the trajectory plans for the studied part (<a href="#jmmp-08-00099-f004" class="html-fig">Figure 4</a>).</p> Full article ">Figure 6
<p>Contour trajectories for the thin-walled and bulky regions.</p> Full article ">Figure 7
<p>(<b>Left</b> and <b>centre</b>) trajectory patterns designed by the planner using MAB-problem-based algorithm for odd and even layers; (<b>right</b>) corresponding printed part (trajectory starting points are not pointed out because they were randomly selected at each printed layer).</p> Full article ">Figure 8
<p>The printed part with the upper surface machined.</p> Full article ">
21 pages, 6440 KiB  
Article
Transfer Learning-Based Artificial Neural Network for Predicting Weld Line Occurrence through Process Simulations and Molding Trials
by Giacomo Baruffa, Andrea Pieressa, Marco Sorgato and Giovanni Lucchetta
J. Manuf. Mater. Process. 2024, 8(3), 98; https://doi.org/10.3390/jmmp8030098 - 9 May 2024
Viewed by 1339
Abstract
Optimizing process parameters to minimize defects remains an important challenge in injection molding (IM). Machine learning (ML) techniques offer promise in this regard, but their application often requires extensive datasets. Transfer learning (TL) emerges as a solution to this problem, leveraging knowledge from [...] Read more.
Optimizing process parameters to minimize defects remains an important challenge in injection molding (IM). Machine learning (ML) techniques offer promise in this regard, but their application often requires extensive datasets. Transfer learning (TL) emerges as a solution to this problem, leveraging knowledge from related tasks to enhance model training and performance. This study explores TL’s viability in predicting weld line visibility in injection-molded components using artificial neural networks (ANNs). TL techniques are employed to transfer knowledge between datasets related to different components. Furthermore, both source datasets obtained from simulations and experimental tests are used during the study. In order to use process simulations to obtain data regarding the presence of surface defects, it was necessary to correlate an output variable of the simulations with the experimental observations. The results demonstrate TL’s efficacy in reducing the data required for training predictive models, with simulations proving to be a cost-effective alternative to experimental data. TL from simulations achieves comparable predictive metric values to those of the non-pre-trained network, but with an 83% reduction in the required data for the target dataset. Overall, transfer learning shows promise in streamlining injection molding optimization and reducing manufacturing costs. Full article
(This article belongs to the Special Issue Advances in Injection Molding: Process, Materials and Applications)
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<p>Part 1 with (<b>right</b>) and without (<b>left</b>) weld line defect.</p> Full article ">Figure 2
<p>Part 2 with (right) and without (left) weld line defect.</p> Full article ">Figure 3
<p>CAD model including part, hot runner system, and cooling channels: (<b>a</b>) Detailed view of the part and insert’s cooling channels, (<b>b</b>) overview of part, cooling channels, and hot runner system.</p> Full article ">Figure 4
<p>Representation of the Part 1 mesh: (<b>a</b>) global view of the mesh and probe points, (<b>b</b>) global view of the back of the component, (<b>c</b>) detailed view of the mesh in the defect formation area.</p> Full article ">Figure 5
<p>Flowchart describing the learning transfer: each step schematically shows how each dataset was obtained and the component to which it refers.</p> Full article ">Figure 6
<p>Result of transfer learning between experimental data of part 1 and experimental data of part 2. Evolution of (<b>a</b>) accuracy, (<b>b</b>) AUC and (<b>c</b>) recall as the number of experimental data used in fine-tuning training.</p> Full article ">Figure 7
<p>Comparison between CN and the SS model obtained from experimental data (SS_TL). Evolution of (<b>a</b>) accuracy, (<b>b</b>) AUC, and (<b>c</b>) recall as the number of experimental data used in fine-tuning training increased.</p> Full article ">Figure 8
<p>Result of transfer learning between synthetic data of part 1 and experimental data of part 2. Evolution of (<b>a</b>) accuracy, (<b>b</b>) AUC, and (<b>c</b>) recall as the number of experimental data used in fine-tuning training increased.</p> Full article ">Figure 9
<p>Comparison between the SS model obtained from simulations (SS_Sym) and the SS model obtained from experimental data (SS_Real). Evolution of (<b>a</b>) accuracy, (<b>b</b>) AUC, and (<b>c</b>) recall as the number of experimental data used in fine-tuning training increased.</p> Full article ">Figure 10
<p>Confusion matrix related to transfer learning from experimental data (60 data points used in the fine-tuning phase).</p> Full article ">Figure 11
<p>Confusion matrix related to transfer learning from synthetic data (60 data points used in the fine-tuning phase).</p> Full article ">Figure 12
<p>Comparison between the SS model related to part 1 (SS_sym_P1) and the SS model about part 2 (SS_sym_P2). Evolution of (<b>a</b>) accuracy, (<b>b</b>) AUC, and (<b>c</b>) recall as the number of experimental data used in fine-tuning training increased.</p> Full article ">
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