Data-driven Self-consistent Clustering Analysis and High-efficiency Cross-scale Simulation of Plastic Forming Process

doi:10.3901/JME.2025.04.074

Abstract

Abstract: Material microstructure deformation determines macroscopic forming performance. How to efficiently link macroscopic forming processes with material microstructure evolution has always been a pressing problem in the field of plastic forming. By mapping the macroscopic mechanical performance requirements of different component positions to the material microstructures, manufacturing processes can be accurately designed and implemented based on the characteristics and distribution of the target microstructures to break the traditional trial-and-error mode and obtain high-performance components with the required shape and performance. Therefore, the strategy of mixed use of cross-scale elements and traditional macro-elements based on data-driven self-consistent clustering analysis is proposed, and the efficient cross-scale simulation technology for plastic forming is developed. The cross-scale forming analysis of DP980 U-shaped components and AA2219 ribbed cylinder is efficiently realized. The material microstructure deformation at key positions during the forming process is predicted, and the computational efficiency can be close to that of traditional macro models. This research provides new ideas for the practical application of cross-scale methods in the process design of large engineering structures.

Key words: plastic forming, high-efficiency cross-scale simulation, data-driven, self-consistent clustering analysis

CLC Number:

TG156

JIANG Shengda, HE Ji, GUO Cong, LI Shuhui, QIAN Changming. Data-driven Self-consistent Clustering Analysis and High-efficiency Cross-scale Simulation of Plastic Forming Process[J]. Journal of Mechanical Engineering, 2025, 61(4): 74-85.

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