南方科技大学知识苑(SUSTech KC): Rapid accomplishment of strength/ductility synergy for additively manufactured Ti-6Al-4V facilitated by machine learning

Title	Rapid accomplishment of strength/ductility synergy for additively manufactured Ti-6Al-4V facilitated by machine learning
Author	Yao, Zhifu 1,2,3; Jia, Xue 2,3; Yu, Jinxin 2,3; Yang, Mujin4 ; Huang, Chao 2,3; Yang, Zhijie 2,3; Wang, Cuiping 5,6; Yang, Tao 7; Wang, Shuai4 ; Shi, Rongpei 2,3; Wei, Jun 3; Liu, Xingjun 1,2,3
Corresponding Author	Wang, Cuiping
Publication Years	2023-01
DOI	10.1016/j.matdes.2022.111559
Source Title	Materials and Design Impact Factor & Quartile
ISSN	0264-1275
EISSN	1873-4197
Volume	225
Abstract	Titanium alloys fabricated by laser powder bed fusion (LPBF) often suffer from limited ductility because of the inherent acicular α′ martensite embedded in the columnar parent phase grains (prior-β grains). The post-built heat treatment at a relatively high temperature (∼1075 K) necessary for decomposing martensite results in improved ductility at the cost of strength. It, however, remains difficult to achieve balances between strength and ductility in as-printed conditions due to the huge range of possible compositions of printing process variables. Herein, using LPBF-processed Ti-6Al-4V (Ti64) alloy as an example, we demonstrate that machine learning (ML) is capable of accelerating the discovery of the proper sets of processing parameters resulting in a superior synergy of strength and ductility (i.e., yield strength, Ys0.2 = 1044 ± 10 MPa, uniform elongation, UEL = 10.5 ± 1.2 % and total elongation = 15 ± 1.5 %). Such property improvement is found to be enabled by an unique refined prior-β grains decorated by confined α′-colony precipitates. In particular, the uniform deformation ability of α′ martensite is improved due to the enhanced microstructure uniformity achieved by weakening variant selection. ML-based processing parameter optimization approach is thus well-positioned to accelerate the qualification of a wide range of L-PBF manufactured alloys beyond Ti-alloys. © 2022
Indexed By	EI
Language	English
SUSTech Authorship	Others
Funding Project	This work is supported by Guangdong Basic and Applied Basic Research Foundation [No. 2021B1515120071], the Shenzhen Science and Technology Program (Grant No. SGDX20210823104002016), the Key-area Research and Development Program of Guang Dong Province [No. 2019B010943001], Development and Reform Commission of Shenzhen Municipality. R. Shi would like to thank the financial support from the open research fund of Songshan Lake Materials Laboratory (2021SLABFK06) and start-up funding from Harbin Institute of Technology (Shenzhen).This work is supported by Guangdong Basic and Applied Basic Research Foundation [No. 2021B1515120071], the Shenzhen Science and Technology Program (Grant No. SGDX20210823104002016), the Key-area Research and Development Program of Guang Dong Province [No. 2019B010943001], Development and Reform Commission of Shenzhen Municipality. R. Shi would like to thank the financial support from the open research fund of Songshan Lake Materials Laboratory (2021SLABFK06) and start-up funding from Harbin Institute of Technology (Shenzhen). This TEM analysis in this work was supported by Sinoma Institute of Materials Research (Guang Zhou) Co. Ltd (SIMR). The authors also would like to thank Kehui Han from Shiyanjia Lab (www.shiyanjia.com) for the EBSD analysis.This TEM analysis in this work was supported by Sinoma Institute of Materials Research (Guang Zhou) Co., Ltd (SIMR). The authors also would like to thank Kehui Han from Shiyanjia Lab (www.shiyanjia.com) for the EBSD analysis.
Publisher	Elsevier Ltd
EI Accession Number	20230113336622
EI Keywords	Ductility ; Economic and social effects ; Machine learning ; Martensite ; Ternary alloys ; Titanium alloys
ESI Classification Code	Metallography:531.2 ; Aluminum Alloys:541.2 ; Titanium and Alloys:542.3 ; Artificial Intelligence:723.4 ; Materials Science:951 ; Social Sciences:971
Data Source	EV Compendex
Citation statistics	Cited Times [WOS]:0
Document Type	Journal Article
Identifier	http://kc.sustech.edu.cn/handle/2SGJ60CL/519672
Department	Department of Mechanical and Energy Engineering
Affiliation	1.State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Shenzhen; 518055, China 2.Institute of Materials Genome & Big Data, Harbin Institute of Technology, Shenzhen; 518055, China 3.Department of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen; 518055, China 4.Department of Mechanical and Energy Engineering, Southern University of Science and Technology, 1088 Xueyuan Blvd, Shenzhen; 518055, China 5.Fujian Key Laboratory of Surface and Interface Engineering for High Performance Materials (Xiamen University), Fujian, Xiamen; 361000, China 6.Xiamen Key Laboratory of High Performance Metals and Materials (Xiamen University), Fujian, Xiamen; 361000, China 7.Department of Material Science and Engineering, College of Engineering, City University of Hong Kong, Hong Kong
Recommended Citation GB/T 7714	Yao, Zhifu,Jia, Xue,Yu, Jinxin,et al. Rapid accomplishment of strength/ductility synergy for additively manufactured Ti-6Al-4V facilitated by machine learning[J]. Materials and Design,2023,225.
APA	Yao, Zhifu.,Jia, Xue.,Yu, Jinxin.,Yang, Mujin.,Huang, Chao.,...&Liu, Xingjun.(2023).Rapid accomplishment of strength/ductility synergy for additively manufactured Ti-6Al-4V facilitated by machine learning.Materials and Design,225.
MLA	Yao, Zhifu,et al."Rapid accomplishment of strength/ductility synergy for additively manufactured Ti-6Al-4V facilitated by machine learning".Materials and Design 225(2023).

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