南方科技大学知识苑(SUSTech KC): Laser Wire Additive Manufacturing of Ti-6Al-4V Alloy and Its Machine Learning Study for Parameters Optimization (Invited)

题名	Laser Wire Additive Manufacturing of Ti-6Al-4V Alloy and Its Machine Learning Study for Parameters Optimization (Invited)
作者	Junyi, Wu 1,2; Bo, Zhang 2; Weihua, Wang 2,3; Weipeng, Li 1; Xiyu, Yao 1; Dawei, Wang 1,4; Wei, Xing 5; Ming, Yan 1,4
通讯作者	Bo, Zhang; Ming, Yan
发表日期	2024-02-01
DOI	10.3788/CJL231439
发表期刊	CHINESE JOURNAL OF LASERS-ZHONGGUO JIGUANG 影响因子和分区
ISSN	0258-7025
卷号	51 期号:4
摘要	Ti-6Al-4V is a benchmark Ti alloy. Laser wire additive manufacturing (LWAM) offers advanced manufacturing capability to the alloy for applications possibly including exploration of outer space. As a typical multiple -variable process, LWAM is complex, which, however, can be analyzed, predicated or even optimized by artificial intelligence (AI) methods such as machine learning (ML). In this study, printing parameters of the Ti-6Al- 4V is firstly optimized using single -track -single -layer experiments, and then single -track -multiple -layer samples are printed, whose properties in terms of hardness and compressive strength are analyzed subsequently by both experiments and ML. The two ML approaches, artificial neural network (ANN) and support vector machine (SVM), are employed to predict the experimental results, whose coefficients of determination R2 show good values. Further optimized properties are realized by adopting genetic algorithm (GA) and simulated annealing (SA) approaches, which contribute to high mechanical properties achieved, for instance, an engineering compressive strength of about 1694 MPa. The results here indicate that important mechanical properties of the LWAM-prepared Ti alloys can be well predicted and enhanced using suitable ML approaches.
关键词	laser technique laser wire additive manufacturing (LWAM) Ti-6Al-4V machine learning mechanical properties support vector machine (SVM) artificial neural network (ANN)
相关链接	[来源记录]
收录类别	ESCI ; EI
语种	英语
学校署名	第一 ; 通讯
WOS研究方向	Optics
WOS类目	Optics
WOS记录号	WOS:001186942300004
出版者	CHINESE LASER PRESS
来源库	Web of Science
引用统计
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/788662
专题	工学院_材料科学与工程系南方科技大学
作者单位	1.Southern Univ Sci & Technol, Dept Mat Sci & Engn, Shenzhen 518055, Guangdong, Peoples R China 2.Songshan Lake Mat Lab, Dongguan 523830, Guangdong, Peoples R China 3.Chinese Acad Sci, Inst Phys, Beijing 100190, Peoples R China 4.Southern Univ Sci & Technol, Jiaxing Res Inst, Shenzhen 518055, Guangdong, Peoples R China 5.Natl Supercomp Ctr, High Performance Comp Dept, Shenzhen 518055, Guangdong, Peoples R China
第一作者单位	材料科学与工程系
通讯作者单位	材料科学与工程系; 南方科技大学
第一作者的第一单位	材料科学与工程系
推荐引用方式 GB/T 7714	Junyi, Wu,Bo, Zhang,Weihua, Wang,et al. Laser Wire Additive Manufacturing of Ti-6Al-4V Alloy and Its Machine Learning Study for Parameters Optimization (Invited)[J]. CHINESE JOURNAL OF LASERS-ZHONGGUO JIGUANG,2024,51(4).
APA	Junyi, Wu.,Bo, Zhang.,Weihua, Wang.,Weipeng, Li.,Xiyu, Yao.,...&Ming, Yan.(2024).Laser Wire Additive Manufacturing of Ti-6Al-4V Alloy and Its Machine Learning Study for Parameters Optimization (Invited).CHINESE JOURNAL OF LASERS-ZHONGGUO JIGUANG,51(4).
MLA	Junyi, Wu,et al."Laser Wire Additive Manufacturing of Ti-6Al-4V Alloy and Its Machine Learning Study for Parameters Optimization (Invited)".CHINESE JOURNAL OF LASERS-ZHONGGUO JIGUANG 51.4(2024).