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

Wu Junyi ¹Zhang Bo ²Wang Weihua ³Li Weipeng ⁴Yao Xiyu ⁴Wang Dawei ⁵Xing Wei ⁶Yan Ming⁵

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作者信息

1. Department of Materials Science and Engineering,Southern University of Science and Technology,Shenzhen 518055,Guangdong,China;Songshan Lake Materials Laboratory,Dongguan 523830,Guangdong,China
2. Songshan Lake Materials Laboratory,Dongguan 523830,Guangdong,China
3. Songshan Lake Materials Laboratory,Dongguan 523830,Guangdong,China;Institute of Physics,Chinese Academy of Sciences,Beijing 100190,China
4. Department of Materials Science and Engineering,Southern University of Science and Technology,Shenzhen 518055,Guangdong,China
5. Department of Materials Science and Engineering,Southern University of Science and Technology,Shenzhen 518055,Guangdong,China;Jiaxing Research Institute,Southern University of Science and Technology,Shenzhen 518055,Guangdong,China
6. High Performance Computing Department,National Supercomputing Center,Shenzhen 518055,Guangdong,China
折叠

Abstract

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.

Key words

laser technique/laser wire additive manufacturing(LWAM)/Ti-6Al-4V/machine learning/mechanical properties/support vector machine(SVM)/artificial neural network(ANN)

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基金项目

国家自然科学基金(51971108)

国家自然科学基金(52271032)

国家重点研发计划(2021YFA0716302)

国防科技基础加强计划项目(2020-JCIQ-ZD-186-01)

深圳市科技创新委员会项目(JCYJ20220818100612027)

松山湖材料实验室开放课题基金(2021SLABFN18)

technical support from SUSTech CRF()

出版年

2024

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

参考文献量31

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