文献类型：期刊文献

英文题名：Integrated computational materials engineering (ICME) for predicting tensile properties of additively manufactured defect-free single-phase high-entropy alloy

作者：Shen, Tao[1,2];Li, Bo[1,2,3];Zhang, Jianrui[1,2,3];Xuan, Fuzhen[1,3]

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

年份：2025

卷号：20

期号：1

外文期刊名：VIRTUAL AND PHYSICAL PROTOTYPING

收录：;EI(收录号：20250117628596);WOS:【SCI-EXPANDED(收录号:WOS:001387625700001)】；

基金：This research work is sponsored by National Natural Science Foundation of China (52175140), Creative Research Groups of the National Natural Science Foundation of China (52321002), AECC Industry-University-Research Cooperation Project (HFZL2023CXY024), and Research Project of Shanghai Municipal Administration for Market Regulation (2023-46).

语种：英文

外文关键词：Laser powder bed fusion; structure-property relationship; cellular automata; machine learning; integrated computation

摘要：In metal-based additive manufacturing (AM), understanding the process-structure-property (PSP) relationship - where the microstructure is tailored to control performance - is crucial for part customisation. Establishing this PSP linkage often requires extensive experimentation and computational analysis. Our study proposes an integrated computational approach for predicting mechanical properties in the laser powder bed fusion (L-PBF) process. This approach combines microstructure evolution simulation, micromechanical property calculations, and machine learning to model the structure-property relationship. We also introduce a novel sampling method based on two-dimensional (2D) cross-sections with grain-level and slice-level predictive designs. Our findings indicate that microstructure evolution during solidification, modelled via the cellular automata (CA) method and finite volume method (FVM) thermal field, aligns well with experimental results. The proposed model achieves high accuracy in predicting mechanical properties, demonstrating the potential of our approach to advance metal-based AM by streamlining the PSP linkage.