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

题名	Rapid accomplishment of strength/ductility synergy for additively manufactured Ti-6Al-4V facilitated by machine learning
作者	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
通讯作者	Wang, Cuiping
发表日期	2023-01
DOI	10.1016/j.matdes.2022.111559
发表期刊	Materials and Design 影响因子和分区
ISSN	0264-1275
EISSN	1873-4197
卷号	225
摘要	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
关键词	Machine learning Laser powder bed fusion Ti-6Al-4V Strength-ductility trade-off
相关链接	[来源记录]
收录类别	EI ; SCI
语种	英语
学校署名	其他
资助项目	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.
WOS研究方向	Materials Science
WOS类目	Materials Science, Multidisciplinary
WOS记录号	WOS:001020695200001
出版者	Elsevier Ltd
EI入藏号	20230113336622
EI主题词	Ductility ; Economic and social effects ; Machine learning ; Martensite ; Ternary alloys ; Titanium alloys
EI分类号	Metallography:531.2 ; Aluminum Alloys:541.2 ; Titanium and Alloys:542.3 ; Artificial Intelligence:723.4 ; Materials Science:951 ; Social Sciences:971
ESI学科分类	MATERIALS SCIENCE
来源库	EV Compendex
引用统计	被引频次[WOS]：13
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/519672
专题	工学院_机械与能源工程系工学院_材料科学与工程系
作者单位	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
推荐引用方式 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).