文献类型：期刊文献

英文题名：Microstructural feature-driven machine learning for predicting mechanical tensile strength of laser powder bed fusion (L-PBF) additively manufactured Ti6Al4V alloy

作者：Wang, Haijie[1];Li, Bo[1,2,3];Zhang, Wei[1];Xuan, Fuzhen[1,2]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[2]Shanghai Collaborat Innovat Ctr High End Equipment, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Addit Mfg & Intelligent Equipment Res Inst, Shanghai 200237, Peoples R China

年份：2024

卷号：295

外文期刊名：ENGINEERING FRACTURE MECHANICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001138522500001)】；

基金：Acknowledgments This research work is sponsored by National Natural Science Foundation of China (Grant No. 52175140) , National Key R&D Program of China (Grant No. 2022YFB4602102) , Fundamental Research Funds for the Central Universities in China (Grant No.

语种：英文

外文关键词：Laser powder bed fusion; Additive manufacturing; Annealing; Machine learning; Tensile properties; Ti6Al4V

摘要：The rapid solidification inherent in laser powder bed fusion (L-PBF) additive manufacturing (AM) introduces segregation phenomena and formation of non-equilibrium phases in duplex titanium alloy components, thereby impeding their suitability for high-reliability engineering applications. Consequently, heat treatment becomes indispensable for optimizing both the microstructure and mechanical properties to meet application requirements. This study aims to investigate the in-fluence of varied annealing temperatures on the evolution of L-PBF-built Ti6Al4V alloy micro-structure, subsequently elucidating their impact on tensile properties by analyzing of L-PBF process parameters, building orientations, and annealing temperatures. The findings reveal that annealing at 850 degrees C for 2 h facilitates the transformation of brittle martensite into a ductile lamellar (alpha + beta) microstructure, thereby conferring excellent tensile properties upon the L-PBF-built Ti6Al4V alloy. Furthermore, to accurately predict the tensile strengths of the Ti6Al4V, we take into account the L-PBF process parameters and the as-built microstructures in a compre-hensive manner, extracting the pertinent microstructural features. A machine learning (ML)-based model is built to facilitate accurate predictions. Accurate and reliable predictions are demonstrated by this model when applied to Ti6Al4V. This data-driven approach establishes a novel avenue for AM material property prediction and process parameter optimization.