金属增材制造晶体塑性有限胞元自洽聚类分析方法

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.