Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing

Abstract Metal additive manufacturing provides remarkable flexibility in geometry and component design, but localized heating/cooling heterogeneity leads to spatial variations of as-built mechanical properties, significantly complicating the materials design process. To this end, we develop a mechan...

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Autores principales:	Xiaoyu Xie, Jennifer Bennett, Sourav Saha, Ye Lu, Jian Cao, Wing Kam Liu, Zhengtao Gan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/1a6a60506a7b450a9fccb2e8b2a9a37b
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spelling	oai:doaj.org-article:1a6a60506a7b450a9fccb2e8b2a9a37b2021-12-02T17:52:21ZMechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing10.1038/s41524-021-00555-z2057-3960https://doaj.org/article/1a6a60506a7b450a9fccb2e8b2a9a37b2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00555-zhttps://doaj.org/toc/2057-3960Abstract Metal additive manufacturing provides remarkable flexibility in geometry and component design, but localized heating/cooling heterogeneity leads to spatial variations of as-built mechanical properties, significantly complicating the materials design process. To this end, we develop a mechanistic data-driven framework integrating wavelet transforms and convolutional neural networks to predict location-dependent mechanical properties over fabricated parts based on process-induced temperature sequences, i.e., thermal histories. The framework enables multiresolution analysis and importance analysis to reveal dominant mechanistic features underlying the additive manufacturing process, such as critical temperature ranges and fundamental thermal frequencies. We systematically compare the developed approach with other machine learning methods. The results demonstrate that the developed approach achieves reasonably good predictive capability using a small amount of noisy experimental data. It provides a concrete foundation for a revolutionary methodology that predicts spatial and temporal evolution of mechanical properties leveraging domain-specific knowledge and cutting-edge machine and deep learning technologies.Xiaoyu XieJennifer BennettSourav SahaYe LuJian CaoWing Kam LiuZhengtao GanNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Xiaoyu Xie Jennifer Bennett Sourav Saha Ye Lu Jian Cao Wing Kam Liu Zhengtao Gan Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing
description	Abstract Metal additive manufacturing provides remarkable flexibility in geometry and component design, but localized heating/cooling heterogeneity leads to spatial variations of as-built mechanical properties, significantly complicating the materials design process. To this end, we develop a mechanistic data-driven framework integrating wavelet transforms and convolutional neural networks to predict location-dependent mechanical properties over fabricated parts based on process-induced temperature sequences, i.e., thermal histories. The framework enables multiresolution analysis and importance analysis to reveal dominant mechanistic features underlying the additive manufacturing process, such as critical temperature ranges and fundamental thermal frequencies. We systematically compare the developed approach with other machine learning methods. The results demonstrate that the developed approach achieves reasonably good predictive capability using a small amount of noisy experimental data. It provides a concrete foundation for a revolutionary methodology that predicts spatial and temporal evolution of mechanical properties leveraging domain-specific knowledge and cutting-edge machine and deep learning technologies.
format	article
author	Xiaoyu Xie Jennifer Bennett Sourav Saha Ye Lu Jian Cao Wing Kam Liu Zhengtao Gan
author_facet	Xiaoyu Xie Jennifer Bennett Sourav Saha Ye Lu Jian Cao Wing Kam Liu Zhengtao Gan
author_sort	Xiaoyu Xie
title	Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing
title_short	Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing
title_full	Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing
title_fullStr	Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing
title_full_unstemmed	Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing
title_sort	mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1a6a60506a7b450a9fccb2e8b2a9a37b
work_keys_str_mv	AT xiaoyuxie mechanisticdatadrivenpredictionofasbuiltmechanicalpropertiesinmetaladditivemanufacturing AT jenniferbennett mechanisticdatadrivenpredictionofasbuiltmechanicalpropertiesinmetaladditivemanufacturing AT souravsaha mechanisticdatadrivenpredictionofasbuiltmechanicalpropertiesinmetaladditivemanufacturing AT yelu mechanisticdatadrivenpredictionofasbuiltmechanicalpropertiesinmetaladditivemanufacturing AT jiancao mechanisticdatadrivenpredictionofasbuiltmechanicalpropertiesinmetaladditivemanufacturing AT wingkamliu mechanisticdatadrivenpredictionofasbuiltmechanicalpropertiesinmetaladditivemanufacturing AT zhengtaogan mechanisticdatadrivenpredictionofasbuiltmechanicalpropertiesinmetaladditivemanufacturing
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Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing

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