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Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing
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Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing

A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 13331

Special Issue Editor

Dr. Antonio Padovano

E-Mail Website
Guest Editor
Department of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende, Italy
Interests: Industry 4.0; simulation modeling; smart operators; sustainable production and logistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industry 4.0 and the advancements in smart manufacturing and digital technologies are revolutionizing the manufacturing industry. With the integration of these technologies, manufacturing processes can be improved in terms of productivity, quality, sustainability, and cost-effectiveness. This Special Issue aims to showcase the latest research and developments in Industry 4.0 and smart materials processing for enhanced manufacturing. This Special Issue seeks to bring together contributions that address the challenges and opportunities of using Industry 4.0 and smart materials processing to support manufacturing processes. The purpose of this Special Issue is to provide a comprehensive overview of the latest research and practical applications of Industry 4.0 and smart materials processing in the manufacturing sector. The aim is to promote interdisciplinary collaboration and knowledge exchange among researchers, practitioners, and policymakers in the field of manufacturing and materials processing.

Topics of interest include but are not limited to:

  • Additive manufacturing and 3D printing technologies for smart materials processing;
  • Cyber–physical systems and the Industrial Internet of Things (IIoT) in Industry 4.0;
  • Robotics and automation for smart materials processing;
  • Smart sensors and data analytics for process monitoring and optimization;
  • Digital twin and virtual reality for manufacturing simulations and design;
  • Sustainable manufacturing and green technologies for Industry 4.0;
  • Materials innovation and advances in smart materials processing.

Dr. Antonio Padovano
Guest Editor

Manuscript Submission Information

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Published Papers (8 papers)

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Research

Jump to: Review

15 pages, 3553 KiB  
Article
Business Models Definition for Next-Generation Vision Inspection Systems
by Francesco Lupi, Antonio Maffei and Michele Lanzetta
J. Manuf. Mater. Process. 2024, 8(4), 161; https://doi.org/10.3390/jmmp8040161 - 27 Jul 2024
Viewed by 568
Abstract
Automated industrial Visual Inspection Systems (VIS) are predominantly designed for specific use cases, resulting in constrained adaptability, high setup requirements, substantial capital investments, and significant knowledge barriers. This paper explores the business potential of recent alternative architectures proposed in the literature for the [...] Read more.
Automated industrial Visual Inspection Systems (VIS) are predominantly designed for specific use cases, resulting in constrained adaptability, high setup requirements, substantial capital investments, and significant knowledge barriers. This paper explores the business potential of recent alternative architectures proposed in the literature for the visual inspection of individual products or complex assemblies within highly variable production environments, utilizing next-generation VIS. These advanced VIS exhibit significant technical (hardware and software) enhancements, such as increased flexibility, reconfigurability, Computer Aided Design (CAD)-based integration, self-X capabilities, and autonomy, as well as economic improvements, including cost-effectiveness, non-invasiveness, and plug-and-produce capabilities. The new trends in VIS have the potential to revolutionize business models by enabling as-a-service approaches and facilitating a paradigm shift towards more sustainable manufacturing and human-centric practices. We extend the discussion to examine how these technological innovations, which reduce the need for extensive coding skills and lengthy reconfiguration activities for operators, can be implemented as a shared resource within a circular lifecycle. This analysis includes detailing the underlying business model that supports shared utilization among different stakeholders, promoting a circular economy in manufacturing by leveraging the capabilities of next-generation VIS. Such an approach not only enhances the sustainability of manufacturing processes but also democratizes access to state-of-the-art inspection technologies, thereby expanding the possibilities for autonomous manufacturing ecosystems. 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 main properties and related aspects of next-generation VIS. The dotted arrows highlight how autonomy is enabled by certain aspects inherited from the other properties.</p> Full article ">Figure 2
<p>The schematical representation of the pioneering framework proposing the utilization of CAD information for reconfiguring the ReCo file via user-friendly reconfiguration support system (RSS). Image reprinted from Lupi et al., 2023 [<a href="#B6-jmmp-08-00161" class="html-bibr">6</a>] under CC BY 4.0 license.</p> Full article ">Figure 3
<p>The framework for Autonomous-VIS, highlighted within the light gray dotted box. Input/output is shown by light-dotted arrows, while squared boxes represent activities. Module_1 (yellow box) refers to the initial hardware configuration and calibration, which is performed outside of the inspection loop. Modules_2-5 (green boxes) are part of the inspection loop and are connected to other activities outside the scope of the current study (depicted within boxes). Image reprinted from Lupi et al., 2024 [<a href="#B5-jmmp-08-00161" class="html-bibr">5</a>] under CC BY 4.0 license.</p> Full article ">Figure 4
<p>The final version of the next-generation VIS framework, as reprinted from Lupi et al., 2024 [<a href="#B4-jmmp-08-00161" class="html-bibr">4</a>] under CC BY 4.0 license. The red-dotted area denotes the evolution of the framework previously described in <a href="#jmmp-08-00161-f003" class="html-fig">Figure 3</a>. The green area is the CAD-to-ReCo file pipeline.</p> Full article ">Figure 5
<p>Graphical summary of the methodology used in this work to analyze and synthesize the BMs for the next-generation VIS.</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 596
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 ">
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 1016
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 ">
15 pages, 8219 KiB  
Article
An Experiment-Based Variable Compensation Method to Improve the Geometric Accuracy of Sub-mm Features Fabricated by Stereolithography (SLA)
by Francesco Modica, Vito Basile and Irene Fassi
J. Manuf. Mater. Process. 2024, 8(3), 90; https://doi.org/10.3390/jmmp8030090 - 29 Apr 2024
Viewed by 1021
Abstract
In this paper, we present an experimental procedure to enhance the dimensional accuracy of fabrication via stereolithography (SLA) of features at the sub-mm scale. Deviations in sub-mm hemispherical cavity diameters were detected and measured on customized samples by confocal microscopy. The characterization and [...] Read more.
In this paper, we present an experimental procedure to enhance the dimensional accuracy of fabrication via stereolithography (SLA) of features at the sub-mm scale. Deviations in sub-mm hemispherical cavity diameters were detected and measured on customized samples by confocal microscopy. The characterization and experimental observations of samples allowed the identification of inaccuracy sources, mainly due to the laser beam scanning strategy and the incomplete removal of uncured liquid resin in post-processing (i.e., IPA washing). As a technology baseline, the measured dimensional errors on cavity diameters were up to −46%. A compensation method was defined and implemented, resulting in relevant improvements in dimensional accuracy. However, measurements on sub-mm cavities having different sizes revealed that a constant compensation parameter (i.e., C = 85, 96, 120 μm) is not fully effective at the sub-mm scale, where average errors remain at −24%, −18.8%, and −16% for compensations equal to 85, 96 and 120 μm, respectively. A further experimental campaign allowed the identification of an effective nonlinear compensation law where the compensation parameter depends on the sub-mm feature size C = f(D). Results show a sharp improvement in dimensional accuracy on sub-mm cavity fabrication, with errors consistently below +8.2%. The proposed method can be extended for the fabrication of any sub-mm features without restrictions on the specific technology implementation. Full article
(This article belongs to the Special Issue Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing)
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<p>Samples of inverse opal FCC lattice structure fabricated via stereolithography. Nominal cavity diameter D = 2 mm [<a href="#B5-jmmp-08-00090" class="html-bibr">5</a>]. Copyright permission licensed by Elsevier.</p> Full article ">Figure 2
<p>Surface sub-mm texturing with different layouts, sub-mm cavity diameters D, and pitch P between hemispherical cavities. (<b>a</b>) Squared layout, D = 800 μm, P = 700 μm.; (<b>b</b>) Squared layout, D = 600 μm, P = 500 μm.; (<b>c</b>) Hexagonal layout, D = 600 μm, P = 650 μm.; (<b>d</b>) Hexagonal layout, D = 800 μm, P = 775 μm.; (<b>e</b>) Hexagonal layout, D = 800 μm, P = 850 μm. Pictures of SLA manufactured samples (top) and confocal acquisition (10×) of the sub-mm textured surface.</p> Full article ">Figure 3
<p>Drawings of samples with sub-mm cavities.</p> Full article ">Figure 4
<p>Image processing of acquisition images. (<b>a</b>) Color threshold algorithm: parameters (<b>top</b>) and result (<b>bottom</b>); (<b>b</b>) analyze particles algorithm: parameters (<b>top</b>) and result (<b>bottom</b>).</p> Full article ">Figure 5
<p>Schematic block diagram of the research workflow.</p> Full article ">Figure 6
<p>Example of laser spot path of a protrusion (<b>a</b>) and a cavity (<b>b</b>).</p> Full article ">Figure 7
<p>Formlabs Preform slicing and dimensional errors due to laser spot path on the feature edge: (<b>a</b>) laser spot path generated by Formlabs Preform slicing software v.3.32.0 for a generic protrusion feature; (<b>b</b>) dimensional error for a protrusion; (<b>c</b>) dimensional error for a cavity.</p> Full article ">Figure 8
<p>Graphs of diameter (<b>a</b>) and depth (<b>b</b>) as functions of nominal values of the printed full-open sub-mm cavities [<a href="#B5-jmmp-08-00090" class="html-bibr">5</a>]. Copyright permission licensed by Elsevier.</p> Full article ">Figure 9
<p>Effects of the constant compensation strategy with different values of the compensation parameter: without compensation C = 0; with constant compensation C = 85 μm; C = 96 μm; C = 120 μm.</p> Full article ">Figure 10
<p>The plot of all experimental data, identification of a second-order interpolation law, and its solution. Nominal values of cavity diameter (orange line); measured diameters (blue line) with data dispersion (standard deviation); second-order polynomial interpolation (dashed blue line) and its equation y = f(x); Curve of compensation parameters (solid grey line).</p> Full article ">Figure 11
<p>Plots of model prediction, variable, and constant compensations. Model prediction (compensated values) of the diameters (blue line); model compensation (yellow solid line) and its second-order interpolation (yellow dashed line) with its equation (inset); constant compensation equal to the nominal laser spot diameter (black dashed line).</p> Full article ">Figure 12
<p>A picture of the four samples with variable compensation. Each sample has three patterns of sub-mm cavities with varying diameters, from 2 mm down to 500 μm.</p> Full article ">Figure 13
<p>Plots of the error percentage on cavity diameters, without compensation (orange curve), with constant C = 85 μm (green curve) and variable compensations (blue and yellow curves).</p> Full article ">
13 pages, 4353 KiB  
Article
Additive In-Time Manufacturing of Customised Orthoses
by Christian Friedrich, Stephan Rothstock, Laura Slabon and Steffen Ihlenfeldt
J. Manuf. Mater. Process. 2024, 8(2), 63; https://doi.org/10.3390/jmmp8020063 - 21 Mar 2024
Viewed by 1630
Abstract
Additive manufacturing of plastic components in medical technology enables greater freedom of design when designing patient-specific products, in particular, in production of customised medical products, such as orthoses. In the present contribution, the advantages of a digital process chain are combined, from the [...] Read more.
Additive manufacturing of plastic components in medical technology enables greater freedom of design when designing patient-specific products, in particular, in production of customised medical products, such as orthoses. In the present contribution, the advantages of a digital process chain are combined, from the 3D scan of the patient to CAD-supported modelling of the corrective form and the orthosis design until the path planning of a printable geometry. The main disadvantages of current additive printing techniques, such as the fused filament fabrication (FFF) process, are high printing times (>12 h) for larger components as well as the low degree of freedom in the 2.5D printing technique that prevent the subsequent application of geometry features to the product. The fast SEAMHex (Screw Extrusion Additive Manufacturing) printing technology with a hexapod kinematic printing bed provides a solution to the mentioned difficulties. Consequently, the high-performance printer has been prepared for the individual requirements of medical technology in terms of materials and geometries. An effective additive manufacturing process has been realised and tested in combination with a digital process chain for orthosis modelling. Full article
(This article belongs to the Special Issue Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing)
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Figure 1

Figure 1
<p>Currently established orthosis production process and data flow.</p> Full article ">Figure 2
<p>Long-term goal of orthosis production and digitalised data flow.</p> Full article ">Figure 3
<p>SEAMHex2 3D Printer.</p> Full article ">Figure 4
<p>2D middle surfaces of the orthoses, separately for the orthosis surface (light grey) and the support (dark grey).</p> Full article ">Figure 5
<p>Graphical user interface (GUI) of the modelling software for orthoses and braces.</p> Full article ">Figure 6
<p>Representation of a Voronoi pattern based on a Poisson disc distribution for different discrete cell distances (parametrically adjustable) integrated into a brace volume model.</p> Full article ">Figure 7
<p>Representation of a brace with subsequent integration of pattern and functional elements such as (<b>A</b>) closure openings, (<b>B</b>) breathing patterns, or (<b>C</b>) letters.</p> Full article ">Figure 8
<p>Polymesh brace (<b>left</b>), UV mesh (<b>centre</b>); details of the UV mesh (<b>right</b>).</p> Full article ">Figure 9
<p>Tool paths, iterative improvement from left to right by omitting the support structures, compared to <a href="#jmmp-08-00063-f004" class="html-fig">Figure 4</a>.</p> Full article ">Figure 10
<p>Closed orthosis with support structures at the SEAMHex 1 (<b>left</b>); continuous printing with cut-outs using the bypass system at the SEAMHex 2 (<b>right</b>).</p> Full article ">Figure 11
<p>3D-printed strips from printing tests with varying parameters using a main nozzle of 2 mm diameter and the material Akromid PA6CF40 (<b>left</b>); exemplary evaluation of the printing tests with regard to spindle feed, feed forward, and bead width (<b>right</b>).</p> Full article ">Figure 12
<p>Non-post-processed print result without support structures (<b>left</b>); final post-processed 3D printed orthosis (<b>right</b>).</p> Full article ">
0 pages, 2479 KiB  
Article
Innovative Smart Drilling with Critical Event Detection and Material Classification
by Kantawatchr Chaiprabha and Ratchatin Chancharoen
J. Manuf. Mater. Process. 2023, 7(5), 155; https://doi.org/10.3390/jmmp7050155 - 23 Aug 2023
Viewed by 1914
Abstract
This work presents a cyber-physical drilling machine that incorporates technologies discovered in the fourth industrial revolution. The machine is designed to realize its state by detecting whether it hits or breaks through the workpiece, without the need for additional sensors apart from the [...] Read more.
This work presents a cyber-physical drilling machine that incorporates technologies discovered in the fourth industrial revolution. The machine is designed to realize its state by detecting whether it hits or breaks through the workpiece, without the need for additional sensors apart from the position sensor. Such self-recognition enables the machine to adapt and shift the controllers that handle position, velocity, and force, based on the workpiece and the drilling environment. In the experiment, the machine can detect and switch controls that follow the drilling events (HIT and BREAKHTROUGH) within 0.1 and 0.5 s, respectively. The machine’s high visibility design is beneficial for classification of the workpiece material. By using a support-vector-machine (SVM) on thrust force and feed rate, the authors are seen to achieve 92.86% accuracy for classification of material, such as medium-density fiberboard (MDF), acrylic, and glass. Full article
(This article belongs to the Special Issue Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing)
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Figure 1
<p>Force analysis of the wedge model of a lead screw with lead angle (<math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">α</mi> </mrow> </semantics> </math>) and radius (<math display="inline"> <semantics> <mrow> <mi mathvariant="normal">r</mi> </mrow> </semantics> </math>). The blue shape is a carriage, the light-blue shape is an imaginary unwrapped spiral tooth of screw, and <math display="inline"> <semantics> <mrow> <mi mathvariant="normal">x</mi> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">θ</mi> </mrow> </semantics> </math> are translational and rotational motion axis, respectively. The mass of screw and carriage are <math display="inline"> <semantics> <mrow> <mi mathvariant="normal">M</mi> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi mathvariant="normal">m</mi> </mrow> </semantics> </math>, respectively. Actuation torque is denoted by <math display="inline"> <semantics> <mrow> <mi mathvariant="normal">T</mi> </mrow> </semantics> </math> and load force is denoted by <math display="inline"> <semantics> <mrow> <mi mathvariant="normal">P</mi> </mrow> </semantics> </math>. The resultant reaction force between screw and carriage is denoted by <math display="inline"> <semantics> <mrow> <mi mathvariant="normal">R</mi> </mrow> </semantics> </math>. Two directions of friction cone <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">γ</mi> </mrow> </semantics> </math> are shown, depending on the direction of motion.</p> Full article ">Figure 2
<p>The dynamic visibility and mechanical advantage of a lead screw.</p> Full article ">Figure 3
<p>Schema of control diagram: conventional feeding axis of a drilling machine.</p> Full article ">Figure 4
<p>The architecture of the novel drilling machine.</p> Full article ">Figure 5
<p>The actual implementation of a cyber-physical drilling machine.</p> Full article ">Figure 6
<p>The proposed controller for the AI-embedded drilling machine. Blue blocks are processed real-time on micro-controller (STM32F411). Yellow blocks are processed via an edge computer and cloud system. The plant (red) represents a linear motor and a driver, which drives the drill.</p> Full article ">Figure 7
<p>Schema of the experimental behavior of the cyber-physical drilling machine on drilling MDF. Position A refers to the initial contact between the drill and workpiece. Position B denotes the time when HIT is detected. Position C signifies when the tip of the drill bit emerges from the workpiece. Position D refers to when the drill’s velocity exceeds the breakthrough threshold, thereby BREAKTHROUGH is detected.</p> Full article ">Figure 8
<p>Schema of material classification using drilling velocity and force: (<b>a</b>) Scatter plot of all data points and decision areas of SVM classifier (red: MDF, green: acrylic, and blue: glass), and (<b>b</b>) Confusion matrix from test dataset.</p> Full article ">

Review

Jump to: Research

15 pages, 2867 KiB  
Review
Optimizing Milling Parameters for Enhanced Machinability of 3D-Printed Materials: An Analysis of PLA, PETG, and Carbon-Fiber-Reinforced PETG
by Mohamad El Mehtedi, Pasquale Buonadonna, Rayane El Mohtadi, Gabriela Loi, Francesco Aymerich and Mauro Carta
J. Manuf. Mater. Process. 2024, 8(4), 131; https://doi.org/10.3390/jmmp8040131 - 26 Jun 2024
Viewed by 1115
Abstract
Fused deposition modeling (FDM) is widely applied in various fields due to its affordability and ease of use. However, it faces challenges such as achieving high surface quality, precise dimensional tolerance, and overcoming anisotropic mechanical properties. This review analyzes and compares the machinability [...] Read more.
Fused deposition modeling (FDM) is widely applied in various fields due to its affordability and ease of use. However, it faces challenges such as achieving high surface quality, precise dimensional tolerance, and overcoming anisotropic mechanical properties. This review analyzes and compares the machinability of 3D-printed PLA, PETG, and carbon-fiber-reinforced PETG, focusing on surface roughness and burr formation. A Design of Experiments (DoE) with a full-factorial design was used, considering three factors: rotation speed, feed rate, and depth of cut. Each factor had different levels: rotational speed at 3000, 5500, and 8000 rpm; feed rate at 400, 600, and 800 mm/min; and depth of cut at 0.2, 0.4, 0.6, and 0.8 mm. Machinability was evaluated by roughness and burr height using a profilometer for all the materials under the same milling conditions. To evaluate the statistical significance of the influence of various processing parameters on surface roughness and burr formation in 3D-printed components made of three different materials—PLA, PETG, and carbon-fiber-reinforced PETG—an analysis of variance (ANOVA) test was conducted. This analysis investigated whether variations in rotational speed, feed rate, and depth of cut resulted in measurable and significant differences in machinability results. Results showed that milling parameters significantly affect roughness and burr formation, with optimal conditions for minimizing any misalignment highlighting the trade-offs in parameter selection. These results provide insights into the post-processing of FDM-printed materials with milling, indicating the need for a balanced approach to parameter selection based on application-specific requirements. Full article
(This article belongs to the Special Issue Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing)
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<p>(<b>a</b>) dimensions of the samples used for tensile testing and (<b>b</b>) printing directions.</p> Full article ">Figure 2
<p>Geometric dimensions of samples for machining tests.</p> Full article ">Figure 3
<p>Stress-strain curves of PLA, PETG, and CF-PETG in all tested conditions. (<b>a</b>) X, (<b>b</b>) Y, and (<b>c</b>) Z printing direction.</p> Full article ">Figure 4
<p>Bar charts of mechanical properties of all samples in all tested conditions: (<b>a</b>) Young’s modulus, (<b>b</b>) Ultimate Tensile strength, and (<b>c</b>) elongation at break.</p> Full article ">Figure 5
<p>Pareto charts of the standardized effects for roughness of (<b>a</b>) CF-PETG, (<b>b</b>) PETG, and (<b>c</b>) PLA.</p> Full article ">Figure 6
<p>Pareto charts of the standardized effects for burr height of (<b>a</b>) CF-PETG, (<b>b</b>) PETG, and (<b>c</b>) PLA.</p> Full article ">Figure 7
<p>Main effect plots for roughness of PLA, PETG, and CF-PETG for (<b>a</b>) rotational speed, (<b>b</b>) feed rate, and (<b>c</b>) cut depth.</p> Full article ">Figure 8
<p>Main effect plots for burr height of PLA, PETG, and CF-PETG for (<b>a</b>) rotational speed, (<b>b</b>) feed rate, and (<b>c</b>) cut depth.</p> Full article ">
48 pages, 8831 KiB  
Review
Tool Wear Monitoring with Artificial Intelligence Methods: A Review
by Roberto Munaro, Aldo Attanasio and Antonio Del Prete
J. Manuf. Mater. Process. 2023, 7(4), 129; https://doi.org/10.3390/jmmp7040129 - 11 Jul 2023
Cited by 7 | Viewed by 4182
Abstract
Tool wear is one of the main issues encountered in the manufacturing industry during machining operations. In traditional machining for chip removal, it is necessary to know the wear of the tool since the modification of the geometric characteristics of the cutting edge [...] Read more.
Tool wear is one of the main issues encountered in the manufacturing industry during machining operations. In traditional machining for chip removal, it is necessary to know the wear of the tool since the modification of the geometric characteristics of the cutting edge makes it unable to guarantee the quality required during machining. Knowing and measuring the wear of tools is possible through artificial intelligence (AI), a branch of information technology that, by interpreting the behaviour of the tool, predicts its wear through intelligent systems. AI systems include techniques such as machine learning, deep learning and neural networks, which allow for the study, construction and implementation of algorithms in order to understand, improve and optimize the wear process. The aim of this research work is to provide an overview of the recent years of development of tool wear monitoring through artificial intelligence in the general and essential requirements of offline and online methods. The last few years mainly refer to the last ten years, but with a few exceptions, for a better explanation of the topics covered. Therefore, the review identifies, in addition to the methods, the industrial sector to which the scientific article refers, the type of processing, the material processed, the tool used and the type of wear calculated. Publications are described in accordance with PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols). 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 flowchart representing the method followed for data extraction.</p> Full article ">Figure 2
<p>Offline and online measurements [<a href="#B58-jmmp-07-00129" class="html-bibr">58</a>].</p> Full article ">Figure 3
<p>Online measurement scheme.</p> Full article ">Figure 4
<p>Number of publications on milling containing features of signal in different domains.</p> Full article ">Figure 5
<p>Number of publications on turning containing features of signal in different domains.</p> Full article ">Figure 6
<p>Number of publications on drilling containing features of signal in different domains.</p> Full article ">Figure 7
<p>Number of publications containing AI per year.</p> Full article ">Figure 8
<p>Number of publications containing type of AI.</p> Full article ">Figure 9
<p>Number of publications on milling containing the type of AI.</p> Full article ">Figure 10
<p>Number of publications on milling containing the type of signal.</p> Full article ">Figure 11
<p>Number of publications on milling containing the type of wear.</p> Full article ">Figure 12
<p>Number of publications for SVM in milling containing the type of wear.</p> Full article ">Figure 13
<p>Number of publications for SVM in milling containing the type of wear.</p> Full article ">Figure 14
<p>Number of publications for SVM in milling containing the type of domain.</p> Full article ">Figure 15
<p>Number of publications on turning containing the type of AI.</p> Full article ">Figure 16
<p>Number of publications on turning containing the type of signal.</p> Full article ">Figure 17
<p>Number of publications on turning containing the type of wear.</p> Full article ">Figure 18
<p>Number of publications for ANN on turning containing the type of wear.</p> Full article ">Figure 19
<p>Number of publications for ANN on turning containing the type of signal.</p> Full article ">Figure 20
<p>Number of publications for ANN on turning containing the type of domain.</p> Full article ">Figure 21
<p>Number of publications on drilling containing the type of AI.</p> Full article ">Figure 22
<p>Number of publications on drilling containing the type of signals.</p> Full article ">Figure 23
<p>Number of publications on drilling containing the type of wear.</p> Full article ">
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