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21 pages, 5686 KiB  
Article
Shape Anisotropy of Grains Formed by Laser Melting of (CoCuFeZr)17Sm2
by Felix Trauter, Ralf Loeffler, Gerhard Schneider and Dagmar Goll
Metals 2024, 14(9), 1025; https://doi.org/10.3390/met14091025 - 9 Sep 2024
Viewed by 275
Abstract
For permanent magnetic materials, anisotropic microstructures are crucial for maximizing remanence Jr and maximum energy product (BH)max. This also applies to additive manufacturing processes such as laser powder bed fusion (PBF-LB). In PBF-LB processing, the solidification behavior is [...] Read more.
For permanent magnetic materials, anisotropic microstructures are crucial for maximizing remanence Jr and maximum energy product (BH)max. This also applies to additive manufacturing processes such as laser powder bed fusion (PBF-LB). In PBF-LB processing, the solidification behavior is determined by the crystal structure of the material, the substrate, and the melt-pool morphology, resulting from the laser power PL and scanning speed vs. To study the impact of these parameters on the textured growth of grains in the melt-pool, experiments were conducted using single laser tracks on (CoCuFeZr)17Sm2 sintered magnets. A method was developed to quantify this grain shape anisotropy from electron backscatter diffraction (EBSD) analysis. For all grains in the melt-pool, the grain shape aspect ratio (GSAR) is calculated to distinguish columnar (GSAR < 0.5) and equiaxed (GSAR > 0.5) grains. For columnar grains, the grain shape orientation (GSO) is determined. The GSO represents the preferred growth direction of each grain. This method can also be used to reconstruct the temperature gradients present during solidification in the melt-pool. A dependence of the melt-pool aspect ratio (depth/width) on energy input was observed, where increasing energy input (increasing PL, decreasing vs) led to higher aspect ratios. For aspect ratios around 0.3, an optimum for directional columnar growth (93% area fraction) with predominantly vertical growth direction (mean angular deviation of 23.1° from vertical) was observed. The resulting crystallographic orientation is beyond the scope of this publication and will be investigated in future work. Full article
(This article belongs to the Special Issue Laser Processing Technology and Principles of Metal Materials)
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Figure 1
<p>Influence of temperature gradient <span class="html-italic">G</span> and growth rate <span class="html-italic">R</span> on the morphology (<span class="html-italic">G/R</span>) and fineness (<span class="html-italic">G*R</span>) of the solidification microstructure (Adapted from [<a href="#B25-metals-14-01025" class="html-bibr">25</a>]).</p>
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<p>Schematic representation of the laser melt-pool formed by (<b>a</b>) lower and (<b>b</b>) higher energy input in PBF-LB additive manufacturing. For single-track experiments, the melt-pool formation is comparable to the PBF-LB process. Black arrows inside the melt-pool outline the local temperature gradient perpendicular to the melt-pool interface. The resulting microstructure with columnar grains is also shown (Adapted from [<a href="#B28-metals-14-01025" class="html-bibr">28</a>]).</p>
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<p>Procedure for the evaluation of the grain shape anisotropy in the melt-pool from (<b>a</b>) the EBSD scan (IPF map) (<b>b</b>) using manual separation of the melt-pool area, (<b>c</b>,<b>d</b>) grain shape aspect ratio (GSAR) evaluation to separate grains without preferential growth direction, (<b>e</b>) using an ellipsoid fit to determine the longest grain axis and corresponding angle, (<b>f</b>) visualization of the grain shape orientation (GSO) in four angular segments, (<b>g</b>) evaluation of the GSO in a polar diagram. The legend (<b>h</b>) describes the four segments of near-horizontal growth (nhg), diagonal growth (dg), near-vertical growth (nvg), and vertical growth (vg) for the GSO map.</p>
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<p>EBSD analysis and evaluation of the grain shape anisotropy in samples produced with a constant <span class="html-italic">P<sub>L</sub></span> = 200 W and <span class="html-italic">v<sub>s</sub></span> of (<b>a</b>–<b>c</b>) 200 mm/s, (<b>d</b>–<b>f</b>) 400 mm/s, (<b>g</b>–<b>i</b>) 600 mm/s and (<b>j</b>–<b>l</b>) 800 mm/s. (<b>a</b>,<b>d</b>,<b>g</b>,<b>j</b>), the inverse pole figure (IPF) maps, show the crystallographic orientation of the grains. (<b>b</b>,<b>e</b>,<b>h</b>,<b>k</b>) show the grain shape aspect ratio (GSAR) map, indicating columnar grown grains with low GSAR (&gt;0.5) and equiaxed grains with a higher GSAR (&gt;0.5). (<b>c</b>,<b>f</b>,<b>i</b>,<b>l</b>) show the grain shape orientation (GSO) maps, which indicates the direction the grains grew in. Growth direction in the GSO maps is shown in four categories (near-horizontal growth (nhg), diagonal growth (dg), near-vertical growth (nvg), and vertical growth (vg)). A different scale bar is used for (<b>a</b>–<b>c</b>) to account for the much deeper melt-pool.</p>
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<p>Polar plots of the grain shape orientation (GSO) of the samples produced with a constant <span class="html-italic">P<sub>L</sub></span> = 200 W and <span class="html-italic">v<sub>s</sub></span> of (<b>a</b>) 200 mm/s, (<b>b</b>) 400 mm/s, (<b>c</b>) 600 mm/s and (<b>d</b>) 800 mm/s. The graphs show the area fractions of the grains in the angular segments, described in <a href="#sec2-metals-14-01025" class="html-sec">Section 2</a>—Materials and Methods.</p>
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<p>EBSD analysis and evaluation of the grain shape anisotropy in samples produced with a constant <span class="html-italic">v<sub>s</sub></span> = 600 mm/s and <span class="html-italic">P<sub>L</sub></span> of (<b>a</b>–<b>c</b>) 50 W, (<b>d</b>–<b>f</b>) 100 W, (<b>g</b>–<b>i</b>) 150 W and (<b>j</b>–<b>l</b>) 200 W. (<b>a</b>,<b>d</b>,<b>g</b>,<b>j</b>), the inverse pole figure (IPF) maps, show the crystallographic orientation of the grains. (<b>b</b>,<b>e</b>,<b>h</b>,<b>k</b>) show the grain shape aspect ratio (GSAR) map, indicating columnar grown grains with low GSAR (&gt;0.5) and equiaxed grains with a higher GSAR (&gt;0.5). (<b>c</b>,<b>f</b>,<b>i</b>,<b>l</b>) show the grain shape orientation (GSO) maps, which indicates the direction the grains grew in. Growth direction in the GSO maps is shown in four categories (near-horizontal growth (nhg), diagonal growth (dg), near-vertical growth (nvg), and vertical growth (vg)).</p>
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<p>Polar plots of the grain shape orientation (GSO) of the samples produced with a constant <span class="html-italic">v<sub>s</sub></span> = 600 mm/s and <span class="html-italic">P<sub>L</sub></span> of (<b>a</b>) 50 W, (<b>b</b>) 100 W, (<b>c</b>) 150 W, and (<b>d</b>) 200 W. The graphs show the area fractions of the grains in the angular segments described in <a href="#sec2-metals-14-01025" class="html-sec">Section 2</a>—Materials and Methods.</p>
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<p>EBSD analysis and evaluation of the grain shape anisotropy in samples produced with a constant <span class="html-italic">E<sub>L</sub></span> = 0.25 J/mm produced by (<b>a</b>–<b>c</b>) <span class="html-italic">P<sub>L</sub></span> = 50 W and <span class="html-italic">v<sub>s</sub></span> = 200 mm/s, (<b>d</b>–<b>f</b>) <span class="html-italic">P<sub>L</sub></span> = 100 W and <span class="html-italic">v<sub>s</sub></span> = 400 mm/s, (<b>g</b>–<b>i</b>) <span class="html-italic">P<sub>L</sub></span> = 150 W and <span class="html-italic">v<sub>s</sub></span> = 600 mm/s and (<b>j</b>–<b>l</b>) <span class="html-italic">P<sub>L</sub></span> = 200 W and <span class="html-italic">v<sub>s</sub></span> = 800 mm/s. (<b>a</b>,<b>d</b>,<b>g</b>,<b>j</b>), the inverse pole figure (IPF) maps, show the crystallographic orientation of the grains. (<b>b</b>,<b>e</b>,<b>h</b>,<b>k</b>) show the grain shape aspect ratio (GSAR) map, indicating columnar grown grains with low GSAR (&gt;0.5) and equiaxed grains with a higher GSAR (&gt;0.5). (<b>c</b>,<b>f</b>,<b>i</b>,<b>l</b>) show the grain shape orientation (GSO) maps, which indicates the direction the grains grew in. Growth direction in the GSO maps is shown in four categories (near-horizontal growth (nhg), diagonal growth (dg), near-vertical growth (nvg), and vertical growth (vg)).</p>
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<p>Polar plots of the grain shape orientation (GSO) of the samples produced with a constant <span class="html-italic">E<sub>L</sub></span> = 0.25 J/mm produced by (<b>a</b>) <span class="html-italic">P<sub>L</sub></span> = 50 W and <span class="html-italic">v<sub>s</sub></span> = 200 mm/s, (<b>b</b>) <span class="html-italic">P<sub>L</sub></span> = 100 W and <span class="html-italic">v<sub>s</sub></span> = 400 mm/s, (<b>c</b>) <span class="html-italic">P<sub>L</sub></span> = 150 W and <span class="html-italic">v<sub>s</sub></span> = 600 mm/s and (<b>d</b>) <span class="html-italic">P<sub>L</sub></span> = 200 W and <span class="html-italic">v<sub>s</sub></span> = 800 mm/s. The graphs show the area fractions of the grains in the angular segments, described in <a href="#sec2-metals-14-01025" class="html-sec">Section 2</a>—Materials and Methods.</p>
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<p>(<b>a</b>) Melt-pool aspect ratio increases with increasing line energy <span class="html-italic">E<sub>L</sub></span>. (<b>b</b>) The grain size (Feret<sub>max</sub> d90 and Feret<sub>min</sub> d90) increases with increasing line energy. (<b>c</b>) GSAR mean value and grain area fraction with GSAR values &gt; 0.5 (equiaxed grains) in dependence of the melt-pool aspect ratio. GSAR mean value and grain area fraction with GSAR &gt; 0.5 seem to have an optimum for a melt-pool aspect ratio around 0.6. Graphs contain trendlines using a polynomial fit.</p>
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<p>Mean angle deviation of the GSO from strictly vertical growth in dependence of (<b>a</b>) the scan speed at constant laser power, (<b>b</b>) the laser power at constant scan speed, (<b>c</b>) the combination of laser power and scan speed at a constant line energy, and (<b>d</b>) the melt-pool aspect ratio. All graphs contain trendlines using a polynomial fit.</p>
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16 pages, 5548 KiB  
Article
Optimizing Selective Laser Melting of Inconel 625 Superalloy through Statistical Analysis of Surface and Volumetric Defects
by Ali Shahrjerdi, Mojtaba Karamimoghadam, Reza Shahrjerdi, Giuseppe Casalino and Mahdi Bodaghi
Designs 2024, 8(5), 87; https://doi.org/10.3390/designs8050087 - 28 Aug 2024
Viewed by 431
Abstract
This article delves into optimizing and modeling the input parameters for the selective laser melting (SLM) process on Inconel 625. The primary aim is to investigate the microstructure within the interlayer regions post-process optimization. For this study, 100 layers with a thickness of [...] Read more.
This article delves into optimizing and modeling the input parameters for the selective laser melting (SLM) process on Inconel 625. The primary aim is to investigate the microstructure within the interlayer regions post-process optimization. For this study, 100 layers with a thickness of 40 µm each were produced. Utilizing the design of experiments (DOE) methodology and employing the Response Surface Method (RSM), the SLM process was optimized. Input parameters such as laser power (LP) and hatch distance (HD) were considered, while changes in microhardness and roughness, Ra, were taken as the responses. Sample microstructure and surface alterations were assessed via scanning electron microscopy (SEM) analysis to ascertain how many defects and properties of Inconel 625 can be controlled using DOE. Porosity and lack of fusion, which were due to rapid post-powder melting solidification, prompted detailed analysis of the flaws both on the surfaces of and in terms of the internal aspects of the samples. An understanding of the formation of these imperfections can help refine the process for enhanced integrity and performance of Inconel 625 printed material. Even slight directional changes in the columnar dendrite structures are discernible within the layers. The microstructural characteristics observed in these samples are directly related to the parameters of the SLM process. In this study, the bulk samples achieved a microhardness of 452 HV, with the minimum surface roughness recorded at 9.9 µm. The objective of this research was to use the Response Surface Method (RSM) to optimize the parameters to result in the minimum surface roughness and maximum microhardness of the samples. Full article
(This article belongs to the Special Issue Post-manufacturing Testing and Characterization of Materials)
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<p>(<b>a</b>) Schematic of selective laser melting for processing Inconel 625 powder (<b>b</b>) sample images of #1 to #9 (<b>c</b>) dimensions of the samples for length (10 mm) and thickness (4 mm).</p>
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<p>Microstructure analysis of samples #1, #3, and #7 with different input parameters including LP = 150–200 W, and HD = 0.1–0.3 mm (<b>a</b>) sample #1, (<b>b</b>) sample #3, and (<b>c</b>) sample #7.</p>
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<p>Macro structures of SLM sample #1 considering (LP = 200 W, and HD = 0.1 mm) the process parameters.</p>
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<p>The defects on samples #5, #8, and #11 (<b>a</b>) porosities on sample #5, (<b>b</b>,<b>c</b>) lack of fusion of sample #8. (<b>d</b>) Open pore on sample #11.</p>
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<p>(<b>a</b>) Surface roughness diagrams of response surface graph of LP and HD. (<b>b</b>) contour plot of HD and LP.</p>
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<p>Surface appearance of the direction of surface roughness test on sample #9 by considering Ra factor for the measurement.</p>
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<p>(<b>a</b>) Normal plot of residuals; (<b>b</b>) predicted and actual graph; (<b>c</b>) perturbation plot.</p>
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<p>(<b>a</b>) Normal plot of residuals; (<b>b</b>) predicted and actual graph; (<b>c</b>) perturbation plot.</p>
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<p>(<b>a</b>) Microhardness diagrams of response surface graph of LP and HD. (<b>b</b>) Contour plot of LP and HD.</p>
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<p>(<b>a</b>) Normal plot of residuals; (<b>b</b>) predicted and actual graph; (<b>c</b>) perturbation plot.</p>
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<p>(<b>a</b>) Normal plot of residuals; (<b>b</b>) predicted and actual graph; (<b>c</b>) perturbation plot.</p>
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<p>Overlay contour plot of hatch distance and laser power. The yellow area shows the optimum area and the gray area shows the less effective process parameters of the SLM process for Inconel 625.</p>
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17 pages, 19222 KiB  
Article
Characterisation of Fe Distribution in the Liquid–Solid Boundary of Al–Zn–Mg–Si Alloy Using Synchrotron X-ray Fluorescence Microscopy
by He Tian, Dongdong Qu, Nega Setargew, Daniel J. Parker, David J. Paterson, David StJohn and Kazuhiro Nogita
Materials 2024, 17(14), 3583; https://doi.org/10.3390/ma17143583 - 19 Jul 2024
Viewed by 568
Abstract
Al–Zn–Mg–Si alloy coatings have been developed to inhibit the corrosion of cold-rolled steel sheets by offering galvanic and barrier protection to the substrate steel. It is known that Fe deposited from the steel strip modifies the microstructure of the alloy. We cast samples [...] Read more.
Al–Zn–Mg–Si alloy coatings have been developed to inhibit the corrosion of cold-rolled steel sheets by offering galvanic and barrier protection to the substrate steel. It is known that Fe deposited from the steel strip modifies the microstructure of the alloy. We cast samples of Al–Zn–Mg–Si coating alloys containing 0.4 wt% Fe and directionally solidified them using a Bridgman furnace to quantify the effect of this Fe addition between 600 °C and 240 °C. By applying a temperature gradient, growth is encouraged, and by then quenching the sample in coolant, the microstructure may be frozen. These samples were analysed using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) to determine the morphological effects of the Fe distribution across the experimental temperature range. However, due to the sub 1 wt% concentration of Fe, synchrotron X-ray fluorescence microscopy (XFM) was applied to quantitatively confirm the Fe distribution. Directionally solidified samples were scanned at 7.05 keV and 18.5 keV using X-ray fluorescence at the Australian Synchrotron using the Maia array detector. It was found that a mass nucleation event of the Fe-based τ6 phase occurred at 495 °C following the nucleation of the primary α-Al phase as a result of a peritectic reaction with remaining liquid. Full article
(This article belongs to the Special Issue Obtaining and Characterization of New Materials (5th Edition))
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Figure 1
<p>Cooling curve analyses for (<b>a</b>) 0.4 wt% Fe Al–Zn–Mg–Si alloy and (<b>b</b>) 0.1 wt% Fe Al–Zn–Mg–Si alloy.</p>
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<p>DS apparatus used for solidifying AM samples against a temperature gradient. (<b>a</b>) Shows a labelled schematic of the sample and furnace prior to a DS experiment, and (<b>b</b>) shows the positioning after. The cross-hatched portion of the sample in (<b>b</b>) has been re-melted and solidified inside the furnace against the temperature gradient and is the portion of interest.</p>
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<p>SEM EDS image taken using JEOL 7800 showing the presence of the modified τ<sub>5c</sub> phase.</p>
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<p>Wt% of Cr and Ni against distance from the stainless-steel tube wall in a sample where the modified τ<sub>5c</sub> reaction has occurred.</p>
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<p>Wt% of Fe against distance from the stainless-steel tube wall in a sample where the modified τ<sub>5c</sub> reaction has occurred.</p>
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<p>Wt% of Mg, Si, and Fe versus distance from stainless-steel tube wall in a sample where the modified τ<sub>5c</sub> reaction has not occurred.</p>
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<p>SEM EDS capture showing the composition of a directionally solidified Al–Zn–Mg–Si sample. (<b>a</b>) Backscattered electron image (<b>b</b>) Fe content (<b>c</b>) Si content (<b>d</b>) Al content.</p>
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<p>Thermo Calc one-axis equivalent phase diagram for 0.4 wt% Fe-containing Al–Zn–Mg–Si alloy [<a href="#B9-materials-17-03583" class="html-bibr">9</a>].</p>
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<p>Backscattered SEM images of 0.4 wt% Fe-containing Al–Zn–Mg–Si alloy in two regions of the high-temperature zone showing (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mrow> <mn>5</mn> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mn>6</mn> </msub> </mrow> </semantics></math>.</p>
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<p>Full image of Fe distribution in the liquid–solid boundary in a nominal 0.4 wt% Fe-containing Al–Zn–Mg–Si alloy.</p>
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<p>Cropped image of interdendritic Fe concentration in the liquid–solid boundary of a nominal 0.4 wt% Fe-containing Al–Zn–Mg–Si alloy.</p>
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<p>Full image of Fe distribution in the fully solidified (low temperature) region in a nominal 0.4 wt% Fe-containing Al–Zn–Mg–Si alloy.</p>
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<p>XFM spectra for nominal 0.4 wt% Fe-containing Al–Zn–Mg–Si sample.</p>
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<p>XFM line scan for nominal 0.4 wt% Fe-containing Al–Zn–Mg–Si sample in LSB. (<b>a</b>) Shows the deviation between the Fe and Zn concentration along the measurement line. (<b>b</b>) Shows only the concentration of Zn, and (<b>c</b>) shows only the concentration of Fe.</p>
Full article ">Figure 14 Cont.
<p>XFM line scan for nominal 0.4 wt% Fe-containing Al–Zn–Mg–Si sample in LSB. (<b>a</b>) Shows the deviation between the Fe and Zn concentration along the measurement line. (<b>b</b>) Shows only the concentration of Zn, and (<b>c</b>) shows only the concentration of Fe.</p>
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25 pages, 20276 KiB  
Article
Powder Bed Fusion–Laser Beam of IN939: The Effect of Process Parameters on the Relative Density, Defect Formation, Surface Roughness and Microstructure
by Merve Nur Doğu, Muhannad Ahmed Obeidi, Hengfeng Gu, Chong Teng and Dermot Brabazon
Materials 2024, 17(13), 3324; https://doi.org/10.3390/ma17133324 - 5 Jul 2024
Viewed by 1060
Abstract
This study investigates the effects of process parameters in the powder bed fusion–laser beam (PBF-LB) process on IN939 samples. The parameters examined include laser power (160, 180, and 200 W), laser scanning speed (400, 800, and 1200 mm/s), and hatch distance (50, 80, [...] Read more.
This study investigates the effects of process parameters in the powder bed fusion–laser beam (PBF-LB) process on IN939 samples. The parameters examined include laser power (160, 180, and 200 W), laser scanning speed (400, 800, and 1200 mm/s), and hatch distance (50, 80, and 110 μm). The study focuses on how these parameters affect surface roughness, relative density, defect formation, and the microstructure of the samples. Surface roughness analysis revealed that the average surface roughness (Sa) values of the sample ranged from 4.6 μm to 9.5 μm, while the average height difference (Sz) varied from 78.7 μm to 176.7 μm. Furthermore, increasing the hatch distance from 50 μm to 110 μm while maintaining constant laser power and scanning speed led to a decrease in surface roughness. Relative density analysis indicated that the highest relative density was 99.35%, and the lowest was 93.56%. Additionally, the average porosity values were calculated, with the lowest being 0.06% and the highest reaching 9.18%. Although some samples had identical average porosity values, they differed in porosity/mm2 and average Feret size. Variations in relative density and average porosity were noted in samples with the same volumetric energy density (VED) due to different process parameters. High VED led to large, irregular pores in several samples. Microcracks, less than 50 μm in length, were present, indicating solidification cracks. The microstructural analysis of the XZ planes revealed arc-shaped melt pools, columnar elongated grains aligned with the build direction, and cellular structures with columnar dendrites. This study provides insights for optimizing PBF-LB process parameters to enhance the quality of IN939 components. Full article
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<p>Images of the build plate after fabrication and a schematic of the as-built IN939 samples. The XZ plane (parallel to the build direction) and the XY plane (perpendicular to the build direction) are indicated with arrows and dots, respectively.</p>
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<p>Surface roughness profiles of the selected as-built IN939 samples (samples 1, 8, 17, and 21).</p>
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<p>RSM graphs of the surface roughness (μm) versus the different input processing parameters. Hatch distance: (<b>a</b>) 50 μm, (<b>b</b>) 80 μm and (<b>c</b>) 110 μm.</p>
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<p>RSM graphs of the relative density (%) versus the different input processing parameters. Hatch distance: (<b>a</b>) 50 μm, (<b>b</b>) 80 μm and (<b>c</b>) 110 μm.</p>
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<p>The as-polished optical micrographs of as-built samples (1–9) in the XZ plane (parallel to the build direction). Porosity (%) values are indicated on the micrographs (hatch distance: 50 μm).</p>
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<p>The as-polished optical micrographs of as-built samples (10–18) in the XZ plane (parallel to the build direction). Porosity (%) values are indicated on the micrographs (hatch distance: 80 μm).</p>
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<p>The as-polished optical micrographs of as-built samples (19–27) in the XZ plane (parallel to the build direction). Porosity (%) values are indicated on the micrographs (hatch distance: 110 μm).</p>
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<p>Optical micrographs of the XZ planes of the selected as-built samples (1, 8, 11, 12, 14, 17, 21, 25, and 26).</p>
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<p>SEM images of the XZ planes of the selected as-built samples (1, 8, 14, 17, and 26), along with EDS results of the MC-type carbides.</p>
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<p>Optical images of the XZ planes of the as-built IN939 samples (1–27).</p>
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<p>(<b>a</b>) Relative density (%) and (<b>b</b>) average porosity (%) versus VED (J/mm<sup>3</sup>) graphs of the as-built samples.</p>
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<p>The as-polished optical micrographs of as-built samples (1–27) in the XY plane (perpendicular to the build direction). Porosity (%) values are indicated on the micrographs.</p>
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<p>Optical micrographs of the XZ planes of the selected as-built samples (1, 8, 14, 17, and 26).</p>
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23 pages, 5792 KiB  
Article
Parametric Control via the Algebraic Expression of Lotus-Type Pore Shapes in Metals
by Liwei Wang, Bo-Yue Lee, Peng-Sheng Wei and Mingming Quan
Materials 2024, 17(12), 3013; https://doi.org/10.3390/ma17123013 - 19 Jun 2024
Viewed by 425
Abstract
Lotus-type porous metals, characterized by low densities, large surface areas, and directional properties, are contemporarily utilized as lightweight, catalytic, and energy-damping materials; heat sinks; etc. In this study, the effects of dimensionless working parameters on the morphology of lotus-type pores in metals during [...] Read more.
Lotus-type porous metals, characterized by low densities, large surface areas, and directional properties, are contemporarily utilized as lightweight, catalytic, and energy-damping materials; heat sinks; etc. In this study, the effects of dimensionless working parameters on the morphology of lotus-type pores in metals during unidirectional solidification were extensively investigated via general algebraic expressions. The independent dimensionless parameters include metallurgical, transport, and geometrical parameters such as Sieverts’ law constant, a partition coefficient, the solidification rate, a mass transfer coefficient, the imposed mole fraction of a solute gas, the total pressure at the top free surface, hydrostatic pressure, a solute transport parameter, inter-pore spacing, and initial contact angle. This model accounts for transient gas pressure in the pore, affected by the solute transfer, gas, capillary, and hydrostatic pressures, and Sieverts’ laws at the bubble cap and top free surface. Solute transport across the cap accounts for solute convection at the cap and the amount of solute rejected by the solidification front into the pore. The shape of lotus-type pores can be described using a proposed fifth-degree polynomial approximation, which captures the major portions between the initial contact angle and the maximum radius at a contact angle of 90 degrees, obtained by conserving the total solute content in the system. The proposed polynomial approximation, along with its working parameters, offers profound insights into the formation and shape of lotus-type pores in metals. It systematically provides deep insights into mechanisms that may not be easily revealed with experimental studies. The prediction of a lotus-type pore shape is thus algebraically achieved in good agreement with the available experimental data and previous analytical results. Full article
(This article belongs to the Section Materials Physics)
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<p>Sketch of lotus-type pores with (<b>a</b>) transverse and (<b>b</b>) longitudinal cross-sections [<a href="#B20-materials-17-03013" class="html-bibr">20</a>].</p>
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<p>(<b>a</b>) Solute transport model and (<b>b</b>) segments for analysis [<a href="#B20-materials-17-03013" class="html-bibr">20</a>].</p>
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<p>Comparisons of (<b>a</b>) polynomials of different degrees, and prediction and measurement of (<b>b</b>) diameter, (<b>c</b>) length, and (<b>d</b>) aspect ratio versus solidification rate for different imposed hydrogen gas pressures during the gasarite solidification of copper [<a href="#B20-materials-17-03013" class="html-bibr">20</a>,<a href="#B22-materials-17-03013" class="html-bibr">22</a>].</p>
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<p>Comparisons of (<b>a</b>) polynomials of different degrees, and prediction and measurement of (<b>b</b>) diameter, (<b>c</b>) length, and (<b>d</b>) aspect ratio versus solidification rate for different imposed hydrogen gas pressures during the gasarite solidification of copper [<a href="#B20-materials-17-03013" class="html-bibr">20</a>,<a href="#B22-materials-17-03013" class="html-bibr">22</a>].</p>
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<p>Predicted shapes of lotus-type pores for different dimensionless Sieverts’ law constants at the (<b>a</b>) bubble cap and (<b>b</b>) top free surface, subject to the given dimensionless working parameters.</p>
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<p>Predicted shapes of lotus-type pores for different dimensionless Sieverts’ law constants at the (<b>a</b>) bubble cap and (<b>b</b>) top free surface, subject to the given dimensionless working parameters.</p>
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<p>Predicted shapes of lotus-type pores for different dimensionless solidification rates at the (<b>a</b>) initial contact angles and (<b>b</b>) maximum radius, subject to the given dimensionless working parameters.</p>
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<p>Predicted shapes of lotus-type pores for different partition coefficients at the (<b>a</b>) initial contact angles and (<b>b</b>) maximum radius, subject to the given dimensionless working parameters.</p>
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<p>Predicted shapes of lotus-type pores for different partition coefficients at the (<b>a</b>) initial contact angles and (<b>b</b>) maximum radius, subject to the given dimensionless working parameters.</p>
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<p>Predicted shapes of lotus-type pores for different dimensionless mass transfer coefficients at the (<b>a</b>) initial contact angles and (<b>b</b>) maximum radius, subject to the given dimensionless working parameters.</p>
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<p>Predicted shapes of lotus-type pores for different dimensionless mass transfer coefficients at the (<b>a</b>) initial contact angles and (<b>b</b>) maximum radius, subject to the given dimensionless working parameters.</p>
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<p>Predicted shapes of lotus-type pores for different mole fractions of the solute gas at the top surface, subject to the given dimensionless working parameters.</p>
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<p>Predicted shapes of lotus-type pores for different dimensionless ambient pressures, subject to the given dimensionless working parameters.</p>
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<p>Predicted shapes of lotus-type pores for different dimensionless hydrostatic pressures, subject to the given dimensionless working parameters.</p>
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<p>Predicted shapes of lotus-type pores for different initial contact angles, subject to the given dimensionless working parameters.</p>
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<p>Predicted shapes of lotus-type pores for different solute transport parameters, subject to the given dimensionless working parameters.</p>
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<p>Predicted shapes of lotus-type pores for different dimensionless inter-pore spacings, subject to the given dimensionless working parameters.</p>
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16 pages, 7208 KiB  
Article
Microstructure and Mechanical Properties of Ti-6Al-4V In Situ Alloyed with 3 wt% Cr by Laser Powder Bed Fusion
by Valerie Sue Goettgens, Luca Weber, Jakob Braun, Lukas Kaserer, Ilse Letofsky-Papst, Stefan Mitsche, David Schimbäck and Gerhard Leichtfried
Metals 2024, 14(6), 715; https://doi.org/10.3390/met14060715 - 16 Jun 2024
Viewed by 1096
Abstract
This work studied the microstructure and mechanical properties of Ti-6Al-4V in situ alloyed with 3 wt% Cr by laser powder bed fusion (LPBF). Specimens with a relative density of 99.14 ± 0.11% were produced, showing keyhole and lack of fusion pores. Due to [...] Read more.
This work studied the microstructure and mechanical properties of Ti-6Al-4V in situ alloyed with 3 wt% Cr by laser powder bed fusion (LPBF). Specimens with a relative density of 99.14 ± 0.11% were produced, showing keyhole and lack of fusion pores. Due to incomplete mixing of the components during melting, chemical inhomogeneities were observed in the solidified material. The addition of Cr promoted thermal supercooling during solidification and induced a reduction in the primary β grain size in the longitudinal direction and a weakening of the otherwise strong ⟨100⟩β texture, both typical issues for Ti-6Al-4V produced by LPBF. The primary β at first transformed martensitically to α’, but by preheating the substrate plate to 500 °C and cyclically reheating the material by melting subsequent layers, in situ martensite decomposition was achieved, resulting in a fine lamellar α + β microstructure. In addition, the B19 phase was detected in the β matrix, presumably caused by Fe impurities in the Cr powder feedstock. Specimens exhibited a hardness of 402 ± 18 HV10, and an excellent ultimate tensile strength of 1450 ± 22 MPa at an elongation at break of 4.5 ± 0.2%. Full article
(This article belongs to the Section Additive Manufacturing)
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<p>OM image of the sample (<b>a</b>) showing keyhole (<b>b</b>) and lack of fusion porosity (<b>c</b>).</p>
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<p>XRD pattern of the top layer and the cross section of the LPBF specimen.</p>
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<p>SEM BSE images of the LPBF specimen center. (<b>a</b>) Inhomogeneous Cr distribution in the alloy. (<b>b</b>) Lamellar α + β microstructure with an αGB.</p>
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<p>(<b>a</b>) EBSD phase map in the BD. (<b>b</b>) Simultaneously recorded EDS map. (<b>c</b>) IPF map of α. (<b>d</b>) Reconstructed phase map of primary β.</p>
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<p>(<b>a</b>) Reconstructed IPF map of primary β in the BD. (<b>b</b>) Referring inverse pole figures in [001] corresponding to the BD, [100], and [010].</p>
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<p>(<b>a</b>) STEM HAADF with area A and B. (<b>b</b><span class="html-italic">–</span><b>f</b>) EDS maps for Ti, Al, V, Fe, and Cr. In (<b>f</b>), ROI 1–3 are marked.</p>
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<p>(<b>a</b>) ADF of area A (see <a href="#metals-14-00715-f006" class="html-fig">Figure 6</a>a) with areas A-1, A-2, and A-3. (<b>b</b>) FFT of A-1 with α. (<b>c</b>) FFT of A-2 with β. (<b>d</b>) FFT of A-3 with B19.</p>
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<p>(<b>a</b>) ADF of area B (see <a href="#metals-14-00715-f006" class="html-fig">Figure 6</a>a) with areas B-1 and B-2. (<b>b</b>) FFT of B-1 with α. (<b>c</b>) FFT of B-2 with β.</p>
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<p>Tensile curves for Ti-6Al-4V–3Cr for <span class="html-italic">N</span> = 4 specimens in the as-built state and horizontal orientation. For clarity, different colors were chosen.</p>
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<p>UTS and A for Ti-6Al-4V–3Cr compared to literature values for Ti-6Al-4V [<a href="#B3-metals-14-00715" class="html-bibr">3</a>,<a href="#B6-metals-14-00715" class="html-bibr">6</a>,<a href="#B32-metals-14-00715" class="html-bibr">32</a>,<a href="#B33-metals-14-00715" class="html-bibr">33</a>,<a href="#B34-metals-14-00715" class="html-bibr">34</a>,<a href="#B35-metals-14-00715" class="html-bibr">35</a>,<a href="#B36-metals-14-00715" class="html-bibr">36</a>,<a href="#B37-metals-14-00715" class="html-bibr">37</a>].</p>
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18 pages, 22284 KiB  
Article
Purifying High-Purity Copper via Semi-Continuous Directional Solidification: Insights from Numerical Simulations
by Yao Wu, Yunhu Zhang, Long Zeng and Hongxing Zheng
Separations 2024, 11(6), 176; https://doi.org/10.3390/separations11060176 - 5 Jun 2024
Viewed by 801
Abstract
High-purity copper is essential for fabricating advanced microelectronic devices, particularly integrated circuit interconnects. As the industry increasingly emphasizes scalable and efficient purification methods, this study investigates the multi-physics interactions during the semi-continuous directional solidification process, utilizing a Cu-1 wt.%Ag model alloy. Coupled simulation [...] Read more.
High-purity copper is essential for fabricating advanced microelectronic devices, particularly integrated circuit interconnects. As the industry increasingly emphasizes scalable and efficient purification methods, this study investigates the multi-physics interactions during the semi-continuous directional solidification process, utilizing a Cu-1 wt.%Ag model alloy. Coupled simulation calculations examine the spatial distribution patterns of the impurity element silver (Ag) within semi-continuously solidified ingots under varying pulling rates and melt temperatures. The objective is to provide technical insights into the utilization of the semi-continuous directional solidification method for high-purity copper purification. The findings reveal that increasing the pulling rate and melt temperature leads to a downward shift in the solid–liquid interface relative to the mold top during processing. Alongside the primary clockwise vortex flow, a secondary weak vortex emerges near the solid–liquid interface, facilitating the migration of the impurity element Ag toward the central axis and amplifying radial impurity fluctuations. Furthermore, diverse pulling rates and melt temperature conditions unveil a consistent trend along the ingot’s height, which is characterized by an initial increase in average Ag content, followed by stabilization and then a rapid ascent during the late stage of solidification, with higher pulling rates and melt temperatures expediting this rapid ascent. Leveraging these insights, a validation experiment using 4N-grade recycled copper in a small-scale setup demonstrates the effectiveness of the semi-continuous directional solidification process for high-purity copper production, with copper samples extracted at 1/4 and 3/4 ingot heights achieving a 5N purity level of 99.9994 wt.% and 99.9993 wt.%, respectively. Full article
(This article belongs to the Special Issue Novel Applications of Separation Technology)
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<p>(<b>a</b>) Apparatus for the semi-continuous directional solidification process. (<b>b</b>) Internal structure. Key components: 1. Hard-graphite-felt insulation layer; 2. Graphite crucible; 3. Copper melt; 4. Water-cooled copper induction heating coil; 5. Graphite sleeve; 6. Graphite traction disk; 7. Water-cooled copper tube; 8. Water-cooled stainless steel pulling rod.</p>
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<p>Geometric model dimensions and boundary conditions for thermal exchange used in the numerical simulations.</p>
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<p>Simulated spatial and temporal evolution of distribution of the impurity element Ag throughout the semi-continuous directional solidification process. Conditions: melt temperature of 1400 K and a pulling rate of 50 μm/s.</p>
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<p>(<b>a</b>) Simulated axial distribution of the impurity element Ag along the central axis of the copper ingot at different time points. (<b>b</b>) Simulated radial distribution of Ag across various heights of the ingot during the solidification process. Conditions: melt temperature of 1400 K and a pulling rate of 50 μm/s.</p>
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<p>Simulated temporal evolution of the temperature field, flow field, and solid–liquid interface during the solidification process. Melt temperature of 1400 K and a pulling rate of 50 μm/s.</p>
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<p>Simulated axial distribution of the impurity element Ag along the central axis of the copper ingot under various melt temperature and pulling rate conditions.</p>
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<p>Simulated radial distribution of the impurity element Ag at different ingot heights under various melt temperature and pulling rate conditions.</p>
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<p>Simulated spatial distribution of the impurity element Ag within the copper ingot at a solidification height of 250 mm under six distinct melt temperature and pulling rate conditions.</p>
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<p>Simulated characteristics of the temperature field, flow field, and solid–liquid interface at a solidification height of 250 mm under various melt temperature and pulling rate conditions.</p>
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<p>Simulated distribution of the average content of the impurity element Ag along the ingot height under various melt temperature and pulling rate conditions.</p>
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<p>(<b>a</b>) Recycled copper raw material used. (<b>b</b>) High-purity copper ingot obtained by the semi-continuous directional solidification process. Sampling positions along the ingot axial direction for GDMS analysis are indicated.</p>
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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 928
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>
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<p>Laser profile used.</p>
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<p>(<b>a</b>) Schematic and (<b>b</b>) actual view of the experimental setup.</p>
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<p>The melt pool and the approximate contour of the heat-affected zone (HAZ).</p>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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18 pages, 27063 KiB  
Article
Molecular Dynamics Simulation on Solidification Microstructure and Tensile Properties of Cu/SiC Composites
by Wanjun Yan, Yuhang Lu, Tinghong Gao, Junjie Wang, Xin Tang and Nan Wang
Molecules 2024, 29(10), 2230; https://doi.org/10.3390/molecules29102230 - 9 May 2024
Viewed by 949
Abstract
The shape of ceramic particles is one of the factors affecting the properties of metal matrix composites. Exploring the mechanism of ceramic particles affecting the cooling mechanical behavior and microstructure of composites provides a simulation basis for the design of high-performance composites. In [...] Read more.
The shape of ceramic particles is one of the factors affecting the properties of metal matrix composites. Exploring the mechanism of ceramic particles affecting the cooling mechanical behavior and microstructure of composites provides a simulation basis for the design of high-performance composites. In this study, molecular dynamics methods are used for investigating the microstructure evolution mechanism in Cu/SiC composites containing SiC particles of different shapes during the rapid solidification process and evaluating the mechanical properties after cooling. The results show that the spherical SiC composites demonstrate the highest degree of local ordering after cooling. The more ordered the formation is of face-centered-cubic and hexagonal-close-packed structures, the better the crystallization is of the final composite and the less the number of stacking faults. Finally, the results of uniaxial tensile in three different directions after solidification showed that the composite containing spherical SiC particles demonstrated the best mechanical properties. The findings of this study provide a reference for understanding the preparation of Cu/SiC composites with different shapes of SiC particles as well as their microstructure and mechanical properties and provide a new idea for the experimental and theoretical research of Cu/SiC metal matrix composites. Full article
(This article belongs to the Topic Advances in Computational Materials Sciences)
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<p>(<b>a</b>) Model diagrams of Cu/SiC composites, three models after copper atom transparency: (<b>b</b>) S1, (<b>c</b>) S2, and (<b>d</b>) S3.</p>
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<p>Radial distribution function [g(r)] of the Cu/SiC composites cooled to 200 K.</p>
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<p>(<b>a</b>) The slice shows the change in the displacement of atoms in the three composite models. (<b>b</b>) is a magnified view of the interface in the red dashed box in (<b>a</b>). The black arrow pointing represents the direction of displacement, and the length represents the size of the displacement. Meanwhile, the closer the color is to red, the stronger the displacement is.</p>
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<p>Volume fractions of FCC and HCP structures in different models at a cooling rate of 10<sup>11</sup> K·s <sup>−1</sup>. (<b>a</b>) FCC structures, (<b>b</b>) HCP structures.</p>
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<p>The structural evolution of FCC and HCP in different models. (<b>a</b>) S1, (<b>b</b>) S2, (<b>c</b>) S3. The green atoms are FCC structures, and the red atoms are HCP structures.</p>
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<p>(<b>a</b>) A DXA snapshot of the model at 200 K, (<b>b</b>) the dislocation distribution of the model at 200 K, and (<b>c</b>) dislocation density variation. Green represents FCC atoms, red represents HCP atoms, and gray represents other atoms. The green lines represent Shockley partial dislocations (Burgers vector 1/6 &lt; 1 1 2 &gt;); the blue lines represent perfect dislocations (Burgers vector 1/2 &lt; 1 1 0 &gt;); the magenta lines represent stair-rod dislocations (Burgers vector 1/6 &lt; 1 1 0 &gt;); the yellow lines represent Hirth dislocations (Burgers vector 1/3 &lt; 1 0 0 &gt;); the light blue lines represent Frank dislocations (Burgers vector 1/3 &lt; 1 1 1 &gt;); the red lines represent other dislocations.</p>
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<p>The dislocation evolution of (<b>a</b>) S1, (<b>b</b>) S2, and (<b>c</b>) S3 during rapid solidification. The green lines represent Shockley partial dislocations (Burgers vector 1/6 &lt; 1 1 2 &gt;); the blue lines represent perfect dislocations (Burgers vector 1/2 &lt; 1 1 0 &gt;); the magenta lines represent stair-rod dislocations (Burgers vector 1/6 &lt; 1 1 0 &gt;); the yellow lines represent Hirth dislocations (Burgers vector 1/3 &lt; 1 0 0 &gt;); the light blue lines represent Frank dislocations (Burgers vector 1/3 &lt; 1 1 1 &gt;); the red lines represent other dislocations.</p>
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<p>Dislocation distribution plots of (<b>a</b>) S2 and (<b>b</b>) S3 at 200 K. (<b>a1</b>,<b>b1</b>) are snapshots of the DXA of the slice containing the FCC and HCP structures, and (<b>a2</b>,<b>b2</b>) are snapshots of the slice dislocation evolution. The red atoms are HCP structures. The green lines represent Shockley partial dislocations (Burgers vector 1/6 &lt; 1 1 2 &gt;); the blue lines represent perfect dislocations (Burgers vector 1/2 &lt; 1 1 0 &gt;); the magenta lines represent stair-rod dislocations (Burgers vector 1/6 &lt; 1 1 0 &gt;); the yellow lines represent Hirth dislocations (Burgers vector 1/3 &lt; 1 0 0 &gt;); the light blue lines represent Frank dislocations (Burgers vector 1/3 &lt; 1 1 1 &gt;); the red lines represent other dislocations.</p>
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<p>Stress–strain curves of three models stretched at 200 K for (<b>a</b>) <span class="html-italic">X</span>-axis, (<b>b</b>) <span class="html-italic">Y</span>-axis, and (<b>c</b>) <span class="html-italic">Z</span>-axis. (<b>d</b>) Young’s modulus.</p>
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<p>Deformation evolution of (<b>a</b>) S1, (<b>b</b>) S2, and (<b>c</b>) S3 models under <span class="html-italic">X</span>-axis stretching (slices).</p>
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25 pages, 16462 KiB  
Article
Improved Mechanical Performance in FDM Cellular Frame Structures through Partial Incorporation of Faces
by Mahan Ghosh and Nandika Anne D’Souza
Polymers 2024, 16(10), 1340; https://doi.org/10.3390/polym16101340 - 9 May 2024
Cited by 1 | Viewed by 808
Abstract
The utilization of lattice-type cellular architectures has seen a significant increase, owing to their predictable shape and the ability to fabricate templated porous materials through low-cost 3D-printing methods. Frames based on atomic lattice structures such as face-centered cubic (FCC), body-centered cubic (BCC), or [...] Read more.
The utilization of lattice-type cellular architectures has seen a significant increase, owing to their predictable shape and the ability to fabricate templated porous materials through low-cost 3D-printing methods. Frames based on atomic lattice structures such as face-centered cubic (FCC), body-centered cubic (BCC), or simple cubic (SC) have been utilized. In FDM, the mechanical performance has been impeded by stress concentration at the nodes and melt-solidification interfaces arising from layer-by-layer deposition. Adding plates to the frames has resulted in improvements with a concurrent increase in weight and hot-pocket-induced dimensional impact in the closed cells formed. In this paper, we explore compressive performance from the partial addition of plates to the frames of a SC-BCC lattice. Compression testing of both single unit cells and 4 × 4 × 4 lattices in all three axial directions is conducted to examine stress transfer to the nearest neighbor and assess scale-up stress transfer. Our findings reveal that hybrid lattice structure unit cells exhibit significantly improved modulus in the range of 125% to 393%, specific modulus in the range of 13% to 120%, and energy absorption in the range of 17% to 395% over the open lattice. The scaled-up lattice modulus increased by 8% to 400%, specific modulus by 2% to 107%, and energy absorption by 37% to 553% over the lattice frame. Parameters that emerged as key to improved lightweighting. Full article
(This article belongs to the Section Polymer Applications)
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<p>(<b>a</b>) Stress–Strain response of the unit cells; (<b>b</b>) Stress–strain response of the 4 × 4 × 4 cellular lattice.</p>
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<p>(<b>a</b>) Stress–Strain response of the unit cells; (<b>b</b>) Stress–strain response of the 4 × 4 × 4 cellular lattice.</p>
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<p>(<b>a</b>) Experimental images during compression showing unit-cell fracture; (<b>b</b>) Experimental images during compression showing 4 × 4 × 4 lattice fracture.</p>
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<p>(<b>a</b>) Experimental images during compression showing unit-cell fracture; (<b>b</b>) Experimental images during compression showing 4 × 4 × 4 lattice fracture.</p>
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<p>(<b>a</b>) Yield stress for single and 4 × 4 lattices for all 13 configurations; (<b>b</b>) Elastic Modulus for single and 4 × 4 lattice for all 13 configurations with simulations; (<b>c</b>) Stress contours in unit cell and 4 × 4 × 4 simulations of all 13 lattice types; (<b>d</b>) Stress contours in unit-cell simulations of (i) PLA-0-Y, (ii) PLA-2-O-Z, (iii) PLA-3-Z, and (iv) PLA-4-X at a displacement strain of 0.1 mm.</p>
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<p>(<b>a</b>) Yield stress for single and 4 × 4 lattices for all 13 configurations; (<b>b</b>) Elastic Modulus for single and 4 × 4 lattice for all 13 configurations with simulations; (<b>c</b>) Stress contours in unit cell and 4 × 4 × 4 simulations of all 13 lattice types; (<b>d</b>) Stress contours in unit-cell simulations of (i) PLA-0-Y, (ii) PLA-2-O-Z, (iii) PLA-3-Z, and (iv) PLA-4-X at a displacement strain of 0.1 mm.</p>
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<p>(<b>a</b>) Yield stress for single and 4 × 4 lattices for all 13 configurations; (<b>b</b>) Elastic Modulus for single and 4 × 4 lattice for all 13 configurations with simulations; (<b>c</b>) Stress contours in unit cell and 4 × 4 × 4 simulations of all 13 lattice types; (<b>d</b>) Stress contours in unit-cell simulations of (i) PLA-0-Y, (ii) PLA-2-O-Z, (iii) PLA-3-Z, and (iv) PLA-4-X at a displacement strain of 0.1 mm.</p>
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<p>(<b>a</b>) Yield stress for single and 4 × 4 lattices for all 13 configurations; (<b>b</b>) Elastic Modulus for single and 4 × 4 lattice for all 13 configurations with simulations; (<b>c</b>) Stress contours in unit cell and 4 × 4 × 4 simulations of all 13 lattice types; (<b>d</b>) Stress contours in unit-cell simulations of (i) PLA-0-Y, (ii) PLA-2-O-Z, (iii) PLA-3-Z, and (iv) PLA-4-X at a displacement strain of 0.1 mm.</p>
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<p>(<b>a</b>) SEA of the unit lattice; (<b>b</b>) SEA for the 4 × 4 × 4 lattice.</p>
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<p>(<b>a</b>) Energy absorbed till 15% strain for single cell and 4.5% strain for 4 × 4 × 4; (<b>b</b>) Specific energy absorbed till 15% strain for single cell and 4.5% strain for 4 × 4 × 4.</p>
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<p>(<b>a</b>) Gibson–Ashby plot of PLA unit cells and 4 × 4 × 4 PLA samples; (<b>b</b>) Ashby plot for scaled-up PLA for 4 × 4 × 4.</p>
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<p>(<b>a</b>) Gibson–Ashby plot of PLA unit cells and 4 × 4 × 4 PLA samples; (<b>b</b>) Ashby plot for scaled-up PLA for 4 × 4 × 4.</p>
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12 pages, 5521 KiB  
Article
The Production of Three-Dimensional Metal Objects Using Oscillatory-Strain-Assisted Fine Wire Shaping and Joining
by Anagh Deshpande and Keng Hsu
Materials 2024, 17(10), 2188; https://doi.org/10.3390/ma17102188 - 7 May 2024
Viewed by 2363
Abstract
Material shaping and joining are the two fundamental processes that lie at the core of many forms of metal manufacturing techniques, including additive manufacturing. Current metal additive manufacturing processes such as laser/e-beam powder bed fusion and Directed Energy Deposition predominantly use heat and [...] Read more.
Material shaping and joining are the two fundamental processes that lie at the core of many forms of metal manufacturing techniques, including additive manufacturing. Current metal additive manufacturing processes such as laser/e-beam powder bed fusion and Directed Energy Deposition predominantly use heat and subsequent melt–fusion and solidification to achieve shaping and joining. The energy efficiency of these processes is severely limited due to energy conversion losses before energy is delivered at the point of melt–fusion for shaping and joining, and due to losses through heat transfer to the surrounding environment. This manuscript demonstrates that by using the physical phenomenon of lowered yield stress of metals and enhanced diffusion in the presence of low amplitude high frequency oscillatory strain, metal shaping and joining can be performed in an energy-efficient way. The two performed simultaneously enable a metal additive manufacturing process, namely Resonance-Assisted Deposition (RAD), that has several unique capabilities, like the ability to print net-shape components from hard-to-weld alloys like Al6061 and the ability to print components with a very high aspect ratio. In this study, we show this process’s capabilities by printing solid components using aluminum-based metal alloys. Full article
(This article belongs to the Special Issue Advances in Materials Joining and Additive Manufacturing)
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<p>In this figure, (<b>a</b>) shows a schematic of the experimental setup for Resonance-Assisted Deposition (RAD) 3D printing, (<b>b</b>) shows a representation of the voxel formation process, and (<b>c</b>) shows the tool motion during the RAD process.</p>
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<p>Aluminum 6061 alloy objects fabricated using the RAD process.</p>
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<p>In this figure, (<b>a</b>) shows a bright field TEM image of the interface of pure Al and pure Ni joined using the RAD technique. As mentioned in the manuscript, the initial oscillatory strain breaks the oxide layer from Al. In some cases, the oxide becomes trapped in the inter-layer interface, as shown in the image. A region near the interface with a high density of crystalline defects in Al is clearly seen in this image. High-resolution TEM images of this region with a high defect density are shown in (<b>b</b>,<b>c</b>). The crystalline defects (stacking faults) are apparent in (<b>b</b>). The trapped oxide, which is amorphous in nature, as evident by the diffraction pattern, is shown in (<b>c</b>).</p>
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<p>(<b>a</b>) An Al6061 article printed with the RAD process; (<b>b</b>,<b>c</b>) optical images of surfaces parallel to the XY plane and the XZ plane, respectively, showing the surface texture created during the printing process; (<b>d</b>) a thin-wall sample printed with a high aspect ratio.</p>
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<p>(<b>a</b>) The tool pathing strategy used for printing the components. The strategy uses 2 outer walls and a +45°/−45° infill. The outer walls were printed first, followed by the infill. (<b>b</b>) Optical image of the cross section of a printed component with insufficient overlap (inter-track distance of 0.7 mm); (<b>c</b>) optical image of the cross section of a printed component with sufficient overlap (inter-track distance of 0.6 mm); (<b>d</b>) geometry of an as-printed sample scanned with micro-CT. (<b>e</b>) Internal defect structure in a sample printed with insufficient overlap between adjacent tracks (inter-track distance of 0.7 mm). The red region in this figure shows the size and shape of the defects. (<b>f</b>) Internal defect structure in a sample printed with adequate overlap (inter-track distance of 0.7 mm). The red defect region is significantly reduced with optimized process parameters.</p>
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<p>(<b>a</b>,<b>b</b>) Ultimate tensile strength (UTS) and elongation values for the horizontal and vertical test coupons, respectively; (<b>c</b>) optical image of a horizontal test coupon after tensile testing; (<b>d</b>) optical image of the fracture surface of a horizontal test coupon after tensile testing; (<b>e</b>) optical image of a vertical test coupon after tensile testing; (<b>f</b>) optical image of the fracture surface of a vertical test coupon after tensile testing.</p>
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16 pages, 10891 KiB  
Article
Towards Understanding Formation Mechanism of Cellular Structures in Laser Powder Bed Fused AlSi10Mg
by Xiaoying Zhang, Xingpeng Zhang, Wenbo Liu, Aoke Jiang and Yu Long
Materials 2024, 17(9), 2121; https://doi.org/10.3390/ma17092121 - 30 Apr 2024
Viewed by 960
Abstract
A new approach is proposed that identifies three different zones of the Si-rich network structure (the cellular structure) in laser powder bed fused (LPBF) AlSi10Mg alloy, based on the variation in morphology, grain growth transition, and melt pool solidification conditions. The three identified [...] Read more.
A new approach is proposed that identifies three different zones of the Si-rich network structure (the cellular structure) in laser powder bed fused (LPBF) AlSi10Mg alloy, based on the variation in morphology, grain growth transition, and melt pool solidification conditions. The three identified zones are denoted in the present work as the liquid solidification zone (LSZ), the mushy solidification zone (MSZ), and the heat affected zone (HAZ). The LSZ is the result of liquid–solid transformation, showing small planar growth at the boundary and large cellular growth in the center, while the MSZ is related to a semisolid reaction, and the HAZ arises from a short-time aging process. The boundary between the LSZ and MSZ is identified by the change of grain growth direction and the Si-rich network advancing direction. The boundary between MSZ and HAZ is identified by the start of the breakdown of the Si-rich network. In addition, it is found that the fracture is generated in and propagates along the HAZ during tensile tests. Full article
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<p>An SEM image of AlSi10Mg feedstock powders.</p>
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<p>A schematic representation of samples.</p>
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<p>An OM image of the cross-section of LPBF AlSi10Mg alloy.</p>
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<p>The typical microstructure of the LPBF AlSi10Mg sample. (<b>a</b>) An overview of microstructure near a melt pool boundary; in which four different zones can be identified, i.e., (<b>b</b>) LSZ1, (<b>c</b>) MSZ, (<b>d</b>) HAZ, (<b>e</b>) LSZ2.</p>
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<p>The elemental and phase analysis of LPBF AlSi10Mg sample. (<b>a</b>) A STEM image of network structure; STEM-EDS elemental maps of (<b>b</b>) Al, (<b>c</b>) Si, (<b>d</b>) Mg and (<b>e</b>) Fe for the same area in panel (<b>a</b>); (<b>f</b>) XRD spectrum.</p>
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<p>Geometrical characteristics of the Si-rich network structure in different zones in <a href="#materials-17-02121-f004" class="html-fig">Figure 4</a>a. (<b>a</b>) The processed image with the network highlighted; (<b>b</b>) the minor axis length and the aspect ratio for the Si-rich network structure in different zones.</p>
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<p>The transition in grain growth direction and the change in network structure occurring at the melt pool boundary. (<b>a</b>) An SEM image of melt pool boundary area and the corresponding EBSD IPF map; (<b>b</b>) a close-up of the orange box in panel (<b>a</b>); (<b>c</b>) the network structure in a tri-junction region.</p>
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<p>Microstructural evolution in the laser remelted LPBF AlSi10Mg sample with the distance from the remelted zone. (<b>a</b>–<b>e</b>) SEM images of the evolved microstructure at positions <b><span class="html-italic">a</span></b>–<b><span class="html-italic">e</span></b>, respectively.</p>
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<p>Fracture surfaces of a tensile test sample. (<b>a</b>) The stress–strain curve; (<b>b</b>,<b>c</b>) SEM images of the fracture surface (circled by the orange dashed line); and (<b>d</b>,<b>e</b>) upper side near the fracture surface of the tensile sample (circled by the black dashed line).</p>
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<p>The influence of solidification conditions on the formed microstructure. (<b>a</b>) A modeled temperature field within a melt pool; (<b>b</b>) a close-up of the orange box in panel (<b>a</b>); (<b>c</b>) solidification microstructure selection map (adapted from [<a href="#B2-materials-17-02121" class="html-bibr">2</a>]).</p>
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17 pages, 7329 KiB  
Article
Integrating Real-Time Meteorological Conditions into a Novel Fire Spread Model for Grasslands
by Yakun Zhang, Huimin Yu, Wenjiang Huang, Tiecheng Huang, Meng Fan and Kun Wang
Fire 2024, 7(5), 154; https://doi.org/10.3390/fire7050154 - 26 Apr 2024
Viewed by 1075
Abstract
Accurate comprehension of grassland fires is imperative for maintaining ecological stability. In this study, we propose a novel fire model that incorporates real-time meteorological conditions. Our methodology integrates key meteorological factors including relative humidity, temperature, degree of solidification of combustible materials, and wind [...] Read more.
Accurate comprehension of grassland fires is imperative for maintaining ecological stability. In this study, we propose a novel fire model that incorporates real-time meteorological conditions. Our methodology integrates key meteorological factors including relative humidity, temperature, degree of solidification of combustible materials, and wind speed. These factors are embedded into a comprehensive function that determines both the downwind and upwind spreading speeds of the fire. Additionally, the model accommodates fire spread in the absence of wind by incorporating the direction perpendicular to the wind, with wind speed set to zero. By precisely determining wind speed, the model enables real-time calculation of fire spread speeds in all directions. Under stable wind conditions, the fire spread area typically adopts an elliptical shape. Leveraging ellipse properties, we define the aspect ratio as a function related to wind speed. Consequently, with knowledge of the fire duration, the model accurately estimates the area of fire spread. Our findings demonstrate the effectiveness of this model in predicting and evaluating fires in the Hulunbuir Grassland. The model offers an innovative method for quantifying grassland fires, contributing significantly to the understanding and management of grassland ecosystems. Full article
(This article belongs to the Special Issue Fire Numerical Simulation)
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<p>(<b>a</b>) The image of <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mi>M</mi> </msub> </semantics></math> is obtained when the moisture content (<math display="inline"><semantics> <mrow> <mi>M</mi> <mi>C</mi> </mrow> </semantics></math>) is less than <math display="inline"><semantics> <mrow> <mn>12</mn> <mo>%</mo> </mrow> </semantics></math>. (<b>b</b>) The image of <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mi>M</mi> </msub> </semantics></math> is obtained when the moisture content (<math display="inline"><semantics> <mrow> <mi>M</mi> <mi>C</mi> </mrow> </semantics></math>) is greater than or equal to <math display="inline"><semantics> <mrow> <mn>12</mn> <mo>%</mo> </mrow> </semantics></math> and the wind speed (<span class="html-italic">w</span>) is less than 10 km · h<sup>−1</sup>. (<b>c</b>) The image of <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mi>M</mi> </msub> </semantics></math> is obtained when the moisture content (<math display="inline"><semantics> <mrow> <mi>M</mi> <mi>C</mi> </mrow> </semantics></math>) is greater than or equal to 12% and the wind speed (<span class="html-italic">w</span>) is greater than or equal to 10 km · h<sup>−1</sup>.</p>
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<p>The shape of the fire burning area.</p>
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<p>Spreading velocity analysis of the burning area periphery.</p>
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<p>(<b>a</b>) The image of <math display="inline"><semantics> <mi>λ</mi> </semantics></math> is obtained when the moisture content (<math display="inline"><semantics> <mrow> <mi>M</mi> <mi>C</mi> </mrow> </semantics></math>) is less than <math display="inline"><semantics> <mrow> <mn>12</mn> <mo>%</mo> </mrow> </semantics></math>. (<b>b</b>) The image of <math display="inline"><semantics> <mi>λ</mi> </semantics></math> is obtained when the moisture content (<math display="inline"><semantics> <mrow> <mi>M</mi> <mi>C</mi> </mrow> </semantics></math>) is greater than or equal to <math display="inline"><semantics> <mrow> <mn>12</mn> <mo>%</mo> </mrow> </semantics></math> and the wind speed (<span class="html-italic">w</span>) is less than 10 km · h<sup>−1</sup>. (<b>c</b>) The image of <math display="inline"><semantics> <mi>λ</mi> </semantics></math> is obtained when the moisture content (<math display="inline"><semantics> <mrow> <mi>M</mi> <mi>C</mi> </mrow> </semantics></math>) is greater than or equal to <math display="inline"><semantics> <mrow> <mn>12</mn> <mo>%</mo> </mrow> </semantics></math> and the wind speed (<span class="html-italic">w</span>) is greater than or equal to 10 km · h<sup>−1</sup>.</p>
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<p>The visualization of <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>(</mo> <mi>w</mi> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>Input, process, and output variables in the model.</p>
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<p>The fires occurring in the Hulunbuir Grassland.</p>
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<p>The computed fire spread speed.</p>
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<p>The comparison of the calculations per unit fire spread area among three models.</p>
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<p>Comparison of observed fire spread area and model-predicted fire spread area.</p>
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12 pages, 4550 KiB  
Article
Effect of Initial Position and Crystallographic Orientation on Grain Selection Procedure in Z-Form Selector for Ni-Based Single-Crystal Superalloy
by Yuanyuan Guo, Jun Bao, Xuanning Zhang, Mai Zhang, Xiqiong Yang and Jian Zhang
Materials 2024, 17(8), 1885; https://doi.org/10.3390/ma17081885 - 19 Apr 2024
Viewed by 700
Abstract
The grain selection process in a Z-form selector for Ni-based single-crystal superalloy was simulated using a macro-scale ProCAST software (2013 version) coupled CAFE module combined with an experiment to investigate the grain selection procedure and mechanism with different grain positions and crystal orientation [...] Read more.
The grain selection process in a Z-form selector for Ni-based single-crystal superalloy was simulated using a macro-scale ProCAST software (2013 version) coupled CAFE module combined with an experiment to investigate the grain selection procedure and mechanism with different grain positions and crystal orientation relationships. A non-stationary solidification process was found in the Z-form selector, and the liquid–solid (L–S) interface was tilted in the same direction as the selector channel during directional solidification. Given that the grain boundary was parallel to the Z-form selector, the overgrowth rate of the bi-crystal in the selector channel was very low. The initial position of the bi-crystal in the selector channel has a greater effect on the overgrowth rate than the effects of primary and secondary orientations. The grain selection was a result of the coupling of the competitive grain growth effect and geometrical restriction effect. Finally, the selection grain mechanism within the Z-form selector was discussed, coalescing the temperature field and the grain competition growth mechanism. Full article
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<p>The model of the directional solidification; (<b>a</b>) the coordinate relationship of the casting in the simulation system; (<b>b</b>) the dimensions of the 2D selector; (<b>c</b>) the angle between the thin slice and YOZ plane is 30°; and (<b>d</b>) the FEM mesh for simulation.</p>
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<p>The longitudinal section microstructure of bi-crystal in the Z-form selector (a) and local magnification (<b>b</b>) I region, (<b>c</b>) II region, and (<b>d</b>) III region. The black solid line marks the location of the grain boundary, and the dendrite growth direction is marked by the virtual arrow.</p>
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<p>The temperature profiles of selector with various withdrawal time. (<b>a</b>) 329 s, (<b>b</b>) 422 s, (<b>c</b>) 492 s, (<b>d</b>) 542 s, (<b>e</b>) 592 s, (<b>f</b>) 642 s, (<b>g</b>) 692 s, and (<b>h</b>) 742 s.</p>
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<p>Grain selection process with 30° angle between the thin slice and the YOZ plane. (<b>a</b>) Grain selection process displayed in the longitudinal section and transverse cross section of (<b>b</b>) I, (<b>c</b>) II, (<b>d</b>) III, and (<b>e</b>) IV. The Euler angles of grain at bottom surface A and B were 0°, 0°, 0°, and 0°, 20°, 0°, respectively. The inset in (<b>a</b>) was the crystallographic orientation of bi-crystal shown in the polar figure.</p>
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<p>Grain selection process with different angles between the thin slice and the YOZ plane. The angles were (<b>a1</b>–<b>a5</b>) 0°, (<b>b1</b>–<b>b5</b>) 30°, (<b>c1</b>–<b>c5</b>) 60°, and (<b>d1</b>–<b>d5</b>) 90°. Grain selection process displayed in 3D (<b>a1</b>–<b>d1</b>) and transverse cross section of (<b>a2</b>–<b>d2</b>) I, (<b>a3</b>–<b>d3</b>) II, (<b>a4</b>–<b>d4</b>) III, and (<b>a5</b>–<b>d5</b>) IV. The Euler angles of grain at bottom surface A and B were 0°, 0°, 0°, and 0°, 5°, 0°, respectively.</p>
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<p>Grain selection process of bi-crystal with various primary orientation; (<b>a1</b>–<b>a8</b>) the crystallographic orientation of bi-crystal shown in the polar figure; (<b>b1</b>–<b>b8</b>) grain selection process with the favorably oriented grain located in an area with more growth space; (<b>c1</b>–<b>c8</b>) grain selection process with the unfavorably oriented grain located in an area with more growth space. The Euler angles of favorably oriented grains were 0°, 0°, and 0°; the Euler angles of unfavorably oriented grains were (<b>a1</b>–<b>c1</b>) 0°, −20°, 0°, (<b>a2</b>–<b>c2</b>) 0°, −15°, 0°, (<b>a3</b>–<b>c3</b>) 0°, −10°, 0°, (<b>a4</b>–<b>c4</b>) 0°, −5°, 0°, (<b>a5</b>–<b>c5</b>) 0°, 5°, 0°, (<b>a6</b>–<b>c6</b>) 0°, 10°, 0°, (<b>a7</b>–<b>c7</b>) 0°, 15°, 0°, (<b>a8</b>–<b>c8</b>) 0°, 20°, and 0°.</p>
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<p>Grain selection process of bi-crystal with various secondary orientation of unfavorably oriented grains thorough regulating <span class="html-italic">Φ1</span>; (<b>a1</b>–<b>a6</b>) the crystallographic orientation of bi-crystal shown in the polar figure; (<b>b1</b>–<b>b6</b>) grain selection process. The Euler angles of favorably oriented grains were 0°, 0°, and 0°; the Euler angles of unfavorably oriented grains were (<b>a1</b>–<b>b1</b>) 5°, 15°, 0°, (<b>a2</b>–<b>b2</b>) 15°, 15°, 0°, (<b>a3</b>–<b>b3</b>) 45°, 15°, 0°, (<b>a4</b>–<b>b4</b>) 95°, 15°, 0°, (<b>a5</b>–<b>b5</b>) 120°, 15°, 0°, (<b>a6</b>–<b>b6</b>) 150°, 15°, and 0°.</p>
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<p>Grain selection process of bi-crystal with various secondary orientation of unfavorably oriented grains thorough regulating <span class="html-italic">Φ2</span>; (<b>a1</b>–<b>a6</b>) the crystallographic orientation of bi-crystal shown in the polar figure; (<b>b1</b>–<b>b6</b>) grain selection process. The Euler angles of favorably oriented grains were 0°, 0°, 0°; the Euler angles of unfavorably oriented grains were (<b>a1</b>–<b>b1</b>) 5°, 15°, 5°, (<b>a2</b>–<b>b2</b>) 5°, 15°, 10°, (<b>a3</b>–<b>b3</b>) 5°, 15°, 15°, (<b>a4</b>–<b>b4</b>) 5°, 15°, 25°, (<b>a5</b>–<b>b5</b>) 5°, 15°, 45°, (<b>a6</b>–<b>b6</b>) 5°, 15°, and 60°.</p>
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<p>A schematic diagram of the microstructure evolution of grains within selector at different stage; (<b>a</b>) the L–S interface initially enters the selector; (<b>b</b>) the L–S interface advances to the turn of the selector; (<b>c</b>) the L–S interface is, again, advanced to the tilt of the selector.</p>
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14 pages, 7302 KiB  
Article
Delineating the Ultra-Low Misorientation between the Dislocation Cellular Structures in Additively Manufactured 316L Stainless Steel
by Fei Sun, Yoshitaka Adachi, Kazuhisa Sato, Takuya Ishimoto, Takayoshi Nakano and Yuichiro Koizumi
Materials 2024, 17(8), 1851; https://doi.org/10.3390/ma17081851 - 17 Apr 2024
Viewed by 902
Abstract
Sub-micro dislocation cellular structures formed during rapid solidification break the strength–ductility trade-off in laser powder bed fusion (LPBF)-processed 316L stainless steel through high-density dislocations and segregated elements or precipitates at the cellular boundaries. The high-density dislocation entangled at the cellular boundary accommodates solidification [...] Read more.
Sub-micro dislocation cellular structures formed during rapid solidification break the strength–ductility trade-off in laser powder bed fusion (LPBF)-processed 316L stainless steel through high-density dislocations and segregated elements or precipitates at the cellular boundaries. The high-density dislocation entangled at the cellular boundary accommodates solidification strains among the cellular structures and cooling stresses through elastoplastic deformation. Columnar grains with cellular structures typically form along the direction of thermal flux. However, the ultra-low misorientations between the adjacent cellular structures and their interactions with the cellular boundary formation remain unclear. In this study, we revealed the ultra-low misorientations between the cellular structures in LPBF-processed 316L stainless steel using conventional electron backscatter diffraction (EBSD), transmission Kikuchi diffraction (TKD), and transmission electron microscopy (TEM). The conventional EBSD and TKD analysis results could provide misorientation angles smaller than 2°, while the resolution mainly depends on the specimen quality and scanning step size, and so on. A TEM technique with higher spatial resolution provides accurate information between adjacent dislocation cells with misorientation angles smaller than 1°. This study presents evidence that the TEM method is the better and more precise analytical method for the misorientation measurement of the cellular structures and provides insights into measuring the small misorientation angles between adjacent dislocation cells and nanograins in nanostructured metals and alloys with ultrafine-grained microstructures. Full article
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<p>(<b>a</b>) Schematic diagram of the LPBF process; (<b>b</b>) LPBF parameters of the two samples.</p>
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<p>SEM images of the solidification microstructure in (<b>a</b>) sample 1 and (<b>b</b>) sample 2.</p>
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<p>EBSD-measured results of sample 1: (<b>a</b>) inverse pole figure (IPF) map plotted along the laser scan direction (SD); (<b>b</b>) kernel average misorientation (KAM) map measured in degrees from 0° to 1°; (<b>c</b>) pole figures (PF) of crystallographic orientations taken from the YZ plane; (<b>d</b>) IPF texture taken from the YZ plane; (<b>e</b>) histogram of misorientation angle distribution; (<b>f</b>) the point-to-point misorientation along the black arrow shown in (<b>a</b>).</p>
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<p>EBSD-measured results of sample 2: (<b>a</b>) inverse pole figure (IPF) map plotted along the laser scan direction (SD); (<b>b</b>) kernel average misorientation (KAM) map measured in degrees from 0° to 1°; (<b>c</b>) pole figures (PF) of crystallographic orientations taken from the YZ plane; (<b>d</b>) IPF texture taken from the YZ plane; (<b>e</b>) histogram of misorientation angle distribution; (<b>f</b>) the point-to-point misorientation along the black arrow shown in (<b>a</b>).</p>
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<p>(<b>a</b>) TEM bright field image of the dislocation cells along the cross-section of the cellular structure in sample 1; (<b>b</b>–<b>e</b>) TKD measurement results of the white square region shown in (<b>a</b>), including IPF map, IQ map, and KAM map with degree ranges from 0° to 2°, and 0° to 1°, respectively.</p>
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<p>(<b>a</b>) TEM bright field image of the dislocation cells along the cross-section of the cellular structure in sample 2; (<b>b</b>–<b>e</b>) TKD measurement results of the white square region shown in (<b>a</b>), including IPF map, IQ map, and KAM map with degree ranges from 0° to 2°, and 0° to 1°, respectively. Red arrows in (<b>a</b>,<b>c</b>,<b>e</b>) indicate the same cell boundary.</p>
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<p>(<b>a</b>) TEM bright field image of dislocation cells and related diffraction pattern of sample 1; (<b>b</b>) schematic diagram of the dislocation cells with indexing from 1 to 20; (<b>c</b>) calculated misorientation angle <span class="html-italic">θ</span> between adjacent cells <span class="html-italic">i</span> and <span class="html-italic">j</span>; (<b>d</b>) histogram of misorientation angle distribution.</p>
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<p>(<b>a</b>) TEM bright field image of dislocation cells and related diffraction pattern of sample 2; (<b>b</b>) schematic diagram of the dislocation cells with indexing from 1 to 30; (<b>c</b>) calculated misorientation angles <span class="html-italic">θ</span> between adjacent cells <span class="html-italic">i</span> and <span class="html-italic">j</span>; (<b>d</b>) histogram of misorientation angle distribution.</p>
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