Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework

Abstract During powder production, the pre-alloyed powder composition often deviates from the target composition leading to undesirable properties of additive manufacturing (AM) components. Therefore, we developed a method to perform high-throughput calculation and uncertainty quantification by usin...

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Autores principales:	Xin Wang, Wei Xiong
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/b45cd9022d2948029f7fae57cec82c1f
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spelling	oai:doaj.org-article:b45cd9022d2948029f7fae57cec82c1f2021-12-02T11:43:49ZUncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework10.1038/s41524-020-00454-92057-3960https://doaj.org/article/b45cd9022d2948029f7fae57cec82c1f2020-12-01T00:00:00Zhttps://doi.org/10.1038/s41524-020-00454-9https://doaj.org/toc/2057-3960Abstract During powder production, the pre-alloyed powder composition often deviates from the target composition leading to undesirable properties of additive manufacturing (AM) components. Therefore, we developed a method to perform high-throughput calculation and uncertainty quantification by using a CALPHAD-based ICME framework (CALPHAD: calculations of phase diagrams, ICME: integrated computational materials engineering) to optimize the composition, and took the high-strength low-alloy steel (HSLA) as a case study. We analyzed the process–structure–property relationships for 450,000 compositions around the nominal composition of HSLA-115. Properties that are critical for the performance, such as yield strength, impact transition temperature, and weldability, were evaluated to optimize the composition. With the same uncertainty as to the initial composition, and optimized average composition has been determined, which increased the probability of achieving successful AM builds by 44.7%. The present strategy is general and can be applied to other alloy composition optimization to expand the choices of alloy for additive manufacturing. Such a method also calls for high-quality CALPHAD databases and predictive ICME models.Xin WangWei XiongNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 6, Iss 1, Pp 1-11 (2020)
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 Xin Wang Wei Xiong Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework
description	Abstract During powder production, the pre-alloyed powder composition often deviates from the target composition leading to undesirable properties of additive manufacturing (AM) components. Therefore, we developed a method to perform high-throughput calculation and uncertainty quantification by using a CALPHAD-based ICME framework (CALPHAD: calculations of phase diagrams, ICME: integrated computational materials engineering) to optimize the composition, and took the high-strength low-alloy steel (HSLA) as a case study. We analyzed the process–structure–property relationships for 450,000 compositions around the nominal composition of HSLA-115. Properties that are critical for the performance, such as yield strength, impact transition temperature, and weldability, were evaluated to optimize the composition. With the same uncertainty as to the initial composition, and optimized average composition has been determined, which increased the probability of achieving successful AM builds by 44.7%. The present strategy is general and can be applied to other alloy composition optimization to expand the choices of alloy for additive manufacturing. Such a method also calls for high-quality CALPHAD databases and predictive ICME models.
format	article
author	Xin Wang Wei Xiong
author_facet	Xin Wang Wei Xiong
author_sort	Xin Wang
title	Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework
title_short	Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework
title_full	Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework
title_fullStr	Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework
title_full_unstemmed	Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework
title_sort	uncertainty quantification and composition optimization for alloy additive manufacturing through a calphad-based icme framework
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/b45cd9022d2948029f7fae57cec82c1f
work_keys_str_mv	AT xinwang uncertaintyquantificationandcompositionoptimizationforalloyadditivemanufacturingthroughacalphadbasedicmeframework AT weixiong uncertaintyquantificationandcompositionoptimizationforalloyadditivemanufacturingthroughacalphadbasedicmeframework
_version_	1718395364264902656

Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework

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