Machine learning-based microstructure prediction during laser sintering of alumina

Abstract Predicting material’s microstructure under new processing conditions is essential in advanced manufacturing and materials science. This is because the material’s microstructure hugely influences the material’s properties. We demonstrate an elegant machine learning algorithm that faithfully...

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Autores principales:	Jianan Tang, Xiao Geng, Dongsheng Li, Yunfeng Shi, Jianhua Tong, Hai Xiao, Fei Peng
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1b792a930ba749c6b0fd702737a7049f
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spelling	oai:doaj.org-article:1b792a930ba749c6b0fd702737a7049f2021-12-02T14:58:45ZMachine learning-based microstructure prediction during laser sintering of alumina10.1038/s41598-021-89816-x2045-2322https://doaj.org/article/1b792a930ba749c6b0fd702737a7049f2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89816-xhttps://doaj.org/toc/2045-2322Abstract Predicting material’s microstructure under new processing conditions is essential in advanced manufacturing and materials science. This is because the material’s microstructure hugely influences the material’s properties. We demonstrate an elegant machine learning algorithm that faithfully predicts the microstructure under new conditions, without the need of knowing the governing laws. We name this algorithm, RCWGAN-GP, which is regression-based conditional generative adversarial networks with Wasserstein loss function and gradient penalty. This algorithm was trained with experimental SEM micrographs from laser-sintered alumina under various laser powers. The RCWGAN-GP realistically regenerates the SEM micrographs under the trained laser powers. Impressively, it also faithfully predicts the alumina’s microstructure under unexplored laser powers. The predicted microstructure features, including the morphology of the sintered particles and the pores, match the experimental SEM micrographs very well. We further quantitatively examined the prediction accuracy of the RCWGAN-GP. We trained the algorithm with computer-created micrograph datasets of secondary-phase growth governed by the well-known Johnson–Mehl–Avrami (JMA) equation. The RCWGAN-GP accurately regenerates the micrographs at the trained time series, in terms of the grains’ shapes, sizes, and spatial distributions. More importantly, the predicted secondary phase fraction accurately follows the JMA curve.Jianan TangXiao GengDongsheng LiYunfeng ShiJianhua TongHai XiaoFei PengNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jianan Tang Xiao Geng Dongsheng Li Yunfeng Shi Jianhua Tong Hai Xiao Fei Peng Machine learning-based microstructure prediction during laser sintering of alumina
description	Abstract Predicting material’s microstructure under new processing conditions is essential in advanced manufacturing and materials science. This is because the material’s microstructure hugely influences the material’s properties. We demonstrate an elegant machine learning algorithm that faithfully predicts the microstructure under new conditions, without the need of knowing the governing laws. We name this algorithm, RCWGAN-GP, which is regression-based conditional generative adversarial networks with Wasserstein loss function and gradient penalty. This algorithm was trained with experimental SEM micrographs from laser-sintered alumina under various laser powers. The RCWGAN-GP realistically regenerates the SEM micrographs under the trained laser powers. Impressively, it also faithfully predicts the alumina’s microstructure under unexplored laser powers. The predicted microstructure features, including the morphology of the sintered particles and the pores, match the experimental SEM micrographs very well. We further quantitatively examined the prediction accuracy of the RCWGAN-GP. We trained the algorithm with computer-created micrograph datasets of secondary-phase growth governed by the well-known Johnson–Mehl–Avrami (JMA) equation. The RCWGAN-GP accurately regenerates the micrographs at the trained time series, in terms of the grains’ shapes, sizes, and spatial distributions. More importantly, the predicted secondary phase fraction accurately follows the JMA curve.
format	article
author	Jianan Tang Xiao Geng Dongsheng Li Yunfeng Shi Jianhua Tong Hai Xiao Fei Peng
author_facet	Jianan Tang Xiao Geng Dongsheng Li Yunfeng Shi Jianhua Tong Hai Xiao Fei Peng
author_sort	Jianan Tang
title	Machine learning-based microstructure prediction during laser sintering of alumina
title_short	Machine learning-based microstructure prediction during laser sintering of alumina
title_full	Machine learning-based microstructure prediction during laser sintering of alumina
title_fullStr	Machine learning-based microstructure prediction during laser sintering of alumina
title_full_unstemmed	Machine learning-based microstructure prediction during laser sintering of alumina
title_sort	machine learning-based microstructure prediction during laser sintering of alumina
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1b792a930ba749c6b0fd702737a7049f
work_keys_str_mv	AT jianantang machinelearningbasedmicrostructurepredictionduringlasersinteringofalumina AT xiaogeng machinelearningbasedmicrostructurepredictionduringlasersinteringofalumina AT dongshengli machinelearningbasedmicrostructurepredictionduringlasersinteringofalumina AT yunfengshi machinelearningbasedmicrostructurepredictionduringlasersinteringofalumina AT jianhuatong machinelearningbasedmicrostructurepredictionduringlasersinteringofalumina AT haixiao machinelearningbasedmicrostructurepredictionduringlasersinteringofalumina AT feipeng machinelearningbasedmicrostructurepredictionduringlasersinteringofalumina
_version_	1718389249872494592

Machine learning-based microstructure prediction during laser sintering of alumina

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