金属增材制造晶体塑性有限胞元自洽聚类分析方法
CRYSTAL PLASTICITY FINITE CELL SELF-CONSISTENT CLUSTERING ANALYSIS METHOD FOR METAL ADDITIVE MANUFACTURING
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摘要: 金属增材制造是一种先进的数字化制造技术, 在高性能及复杂构件快速制备方面具有独特的优势. 然而, 其成形材料微观组织复杂且存在不可避免的制造缺陷, 导致实际制造材料性能与设计性能存在偏差, 亟需发展考虑真实材料微观组织和缺陷的力学性能高效预测方法. 针对该问题, 发展了晶体塑性有限胞元-自洽聚类分析方法, 包括离线数据准备和在线快速计算两个阶段. 其中, 在离线阶段, 采用晶体塑性有限胞元法和聚类算法建立实际微观组织代表体元离散数据; 在线阶段, 采用基于加权余量-子域法的自洽聚类分析和考虑Hall-Petch效应的晶体塑性模型求解了代表体元问题的Lippmann-Schwinger方程, 进而通过应力应变均匀化获得材料的宏观等效力学性能. 通过理想及含不规则孔隙的多晶算例验证了所提出方法的计算精度及高效性; 进一步, 采用该方法研究了激光选区熔融增材制造IN625合金力学性能, 并揭示了工艺参数对其力学性能的影响. 结果表明, 文章工作为金属增材制造成形材料力学性能预测提供了一种高效的计算方法.Abstract: Metal additive manufacturing (AM) is an advanced digital manufacturing technology with distinctive advantages in the rapid fabrication of intricate and high-performance parts. However, there are deviations between the mechanical properties of the as-built material and their intended design counterparts due to the complex microstructure of the fabricated material and the inevitable defects that occur during the manufacturing process. To accurately predict the material properties, employing an efficient numerical method that considers the actual microstructural features is crucial. In this study, a crystal plasticity finite cell-self-consistent clustering analysis (CPFC-SCA) method is proposed. It consists of two distinct calculation stages: an offline stage for data preparation and an online stage for rapid calculations. During the offline stage, the CPFC and a clustering method are integrated to discretize the representative volume element (RVE) of the as-built material microstructure. Subsequently, during the online stage, the SCA derived from the subdomain weighted residual formulation and crystal plasticity involving the Hall-Patch effect are utilized to solve the Lippmann-Schwinger equation of the RVE, and the numerical results are further utilized to determine the effective mechanical properties through the homogenization of stress and strain. Several numerical examples, RVEs with and without the irregular void, are presented to showcase the accuracy and efficiency of the proposed method. Furthermore, we applied the proposed method to numerically address the as-built mechanical properties of additively manufactured IN625 using selective laser melting, and the numerical results shed light on the relationship between the process parameters and the mechanical properties. It is demonstrated that the proposed method is a promising numerical simulation tool with high efficiency in predicting the mechanical properties of materials fabricated by metal additive manufacturing.