文献类型：期刊文献

英文题名：An integrated computation framework for predicting mechanical performance of single-phase alloys manufactured using laser powder bed fusion: A case study of CoCrFeMnNi high-entropy alloy

作者：Shen, Tao[1,2];Li, Bo[1,2,3]

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

年份：2024

卷号：39

外文期刊名：MATERIALS TODAY COMMUNICATIONS

收录：;EI(收录号：20242016081370);WOS:【SCI-EXPANDED(收录号:WOS:001240690200001)】；

基金：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. JKG01231610) , Pre research project of Civil Aerospace Technology (Grant No. D020301) , and Equipment Pre-research Sharing Technology Key Project (Grant No. JZX7Y20210422004601) .

语种：英文

外文关键词：Integrated computation; Machine learning (ML); Cellular automata (CA); Crystal plasticity finite element (CPFE); Laser powder bed fusion (L-PBF); Additive manufacturing (AM)

摘要：Establishing a process-structure-property (PSP) relationship is crucial for understanding and optimizing the mechanical properties of components via laser powder bed fusion (L-PBF) additive manufacturing (AM). However, the parameters pertinent to the PSP relationship are encapsulated in a "black box" with a high dimension, resulting in relying on trial-and-error approaches to elucidate hidden associations becoming impracticable, while the acquisition of microscopic data proves financially burdensome. Thus this paper presents an integrated computation framework based on machine learning (ML) for predicting the tensile yield strengths and post-yield hardening rates of single-phase alloys fabricated through L-PBF, for efficient process optimization purposes. Research herein focuses on single-phase alloys to be built via L-PBF, taking CoCrFeMnNi high-entropy alloy as a study case, and provide an efficient integrated computation solution. The methodology proposed in this paper generates as-built microstructures associated with the varying L-PBF process parameters via cellular automata (CA). Subsequently, the mechanical responses of these microstructures are estimated by employing crystal plasticity finite element (CPFE) simulations. In addition, the approach raised in the paper leverages particle-level microstructure descriptors instead of average feature descriptors for the entire representative volume element (RVE) structure in an ML framework to predict the characteristics of the newly randomly growing grains. This research demonstrate the practicality and instructiveness of the methodology for modeling realistic grains. Then, trained ML model exhibits promising accuracy for the prediction of mechanical properties. This work showcases an integrated computation framework combining advanced materials science and ML techniques for enhancing understanding of the L-PBF process and offering valuable insights into process optimization for L-PBF-built diverse materials.