首页|Laser Wire Additive Manufacturing of Ti-6Al-4V Alloy and Its Machine Learning Study for Parameters Optimization(Invited)

Laser Wire Additive Manufacturing of Ti-6Al-4V Alloy and Its Machine Learning Study for Parameters Optimization(Invited)

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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.

laser techniquelaser wire additive manufacturing(LWAM)Ti-6Al-4Vmachine learningmechanical propertiessupport vector machine(SVM)artificial neural network(ANN)

Wu Junyi、Zhang Bo、Wang Weihua、Li Weipeng、Yao Xiyu、Wang Dawei、Xing Wei、Yan Ming

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Department of Materials Science and Engineering,Southern University of Science and Technology,Shenzhen 518055,Guangdong,China

Songshan Lake Materials Laboratory,Dongguan 523830,Guangdong,China

Institute of Physics,Chinese Academy of Sciences,Beijing 100190,China

Jiaxing Research Institute,Southern University of Science and Technology,Shenzhen 518055,Guangdong,China

High Performance Computing Department,National Supercomputing Center,Shenzhen 518055,Guangdong,China

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国家自然科学基金国家自然科学基金国家重点研发计划国防科技基础加强计划项目深圳市科技创新委员会项目松山湖材料实验室开放课题基金technical support from SUSTech CRF

51971108522710322021YFA07163022020-JCIQ-ZD-186-01JCYJ202208181006120272021SLABFN18

2024

10.3788/CJL231439

中国激光
中国光学学会　中科院上海光机所

中国激光

CSTPCD北大核心
影响因子：2.204
ISSN：0258-7025
年,卷(期)：2024.51(4)
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