https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Zhao_et_al_2024a Zhao et al 2024a - Revision history 2026-05-08T21:09:49Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 https://www.scipedia.com/wd/index.php?title=Zhao_et_al_2024a&diff=309458&oldid=prev JSanchez: JSanchez moved page Draft Sanchez Pinedo 197273793 to Zhao et al 2024a 2024-10-23T10:04:22Z <p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_197273793" class="mw-redirect" title="Draft Sanchez Pinedo 197273793">Draft Sanchez Pinedo 197273793</a> to <a href="/public/Zhao_et_al_2024a" title="Zhao et al 2024a">Zhao et al 2024a</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 10:04, 23 October 2024</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table> JSanchez https://www.scipedia.com/wd/index.php?title=Zhao_et_al_2024a&diff=309457&oldid=prev JSanchez at 10:04, 23 October 2024 2024-10-23T10:04:14Z <p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 10:04, 23 October 2024</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l3" >Line 3:</td> <td colspan="2" class="diff-lineno">Line 3:</td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>This study explores the use of machine learning (ML) models in predicting the macroscopic properties of heterogeneous composites. Traditional micromechanics parameters have limitations, thus ML models with and without feature engineering are utilized. For artificial neural network (ANN) models with feature engineering, microstructural descriptors from SEM images of nickel-based superalloys are used to predict hardness. 10 descriptors are selected to reduce the computational cost of the deep neural network (DNN) with the support of the shallow neural network (SNN), and accuracy is enhanced by incorporating two additional descriptors. The result surpasses existing physics-based models. Models without feature engineering employ a convolutional neural network (CNN) to predict the effective thermal conductivity of thermal insulation composite materials. The CNN model demonstrates accurate predictions for novel microstructures. ML models can achieve more efficient predictions than traditional methods, indicating their potential in advancing materials science. In summary, harnessing artificial intelligence to capture the scattering characteristics of heterogeneous materials enables both DNN and CNN models to achieve more efficient predictions compared to traditional methods. This highlights the potential of machine learning in advancing materials science and expediting the development of materials with desired properties.</div></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>This study explores the use of machine learning (ML) models in predicting the macroscopic properties of heterogeneous composites. Traditional micromechanics parameters have limitations, thus ML models with and without feature engineering are utilized. For artificial neural network (ANN) models with feature engineering, microstructural descriptors from SEM images of nickel-based superalloys are used to predict hardness. 10 descriptors are selected to reduce the computational cost of the deep neural network (DNN) with the support of the shallow neural network (SNN), and accuracy is enhanced by incorporating two additional descriptors. The result surpasses existing physics-based models. Models without feature engineering employ a convolutional neural network (CNN) to predict the effective thermal conductivity of thermal insulation composite materials. The CNN model demonstrates accurate predictions for novel microstructures. ML models can achieve more efficient predictions than traditional methods, indicating their potential in advancing materials science. In summary, harnessing artificial intelligence to capture the scattering characteristics of heterogeneous materials enables both DNN and CNN models to achieve more efficient predictions compared to traditional methods. This highlights the potential of machine learning in advancing materials science and expediting the development of materials with desired properties.</div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">== Full Paper ==</ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">&lt;pdf&gt;Media:Draft_Sanchez Pinedo_197273793pap_35.pdf&lt;/pdf&gt;</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:309455:newid:309457 --> </table> JSanchez https://www.scipedia.com/wd/index.php?title=Zhao_et_al_2024a&diff=309455&oldid=prev JSanchez at 10:04, 23 October 2024 2024-10-23T10:04:11Z <p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 10:04, 23 October 2024</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">&#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; </ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">==Abstract==</ins></div></td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">This study explores the use of machine learning (ML) models in predicting the macroscopic properties of heterogeneous composites. Traditional micromechanics parameters have limitations, thus ML models with and without feature engineering are utilized. For artificial neural network (ANN) models with feature engineering, microstructural descriptors from SEM images of nickel-based superalloys are used to predict hardness. 10 descriptors are selected to reduce the computational cost of the deep neural network (DNN) with the support of the shallow neural network (SNN), and accuracy is enhanced by incorporating two additional descriptors. The result surpasses existing physics-based models. Models without feature engineering employ a convolutional neural network (CNN) to predict the effective thermal conductivity of thermal insulation composite materials. The CNN model demonstrates accurate predictions for novel microstructures. ML models can achieve more efficient predictions than traditional methods, indicating their potential in advancing materials science. In summary, harnessing artificial intelligence to capture the scattering characteristics of heterogeneous materials enables both DNN and CNN models to achieve more efficient predictions compared to traditional methods. This highlights the potential of machine learning in advancing materials science and expediting the development of materials with desired properties.</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:309454:newid:309455 --> </table> JSanchez https://www.scipedia.com/wd/index.php?title=Zhao_et_al_2024a&diff=309454&oldid=prev JSanchez: Created blank page 2024-10-23T10:04:08Z <p>Created blank page</p> <p><b>New page</b></p><div></div> JSanchez