Automatic Identification of Critical Damage Value with In-situ Shearing Test and Notched Plate Tensile Test with Image Analysis

¹ Gifu University, Centre for Advanced Die Engineering and Technology (G-CADET), 1-1 Yanagi-do, Gifu 501-1193, Japan
² Shimane University, Next Generation Tatara Co-Creation Centre (NEXTA), 1060 Nishikawatsu-cho, Matsue 690-8504, Japan

^* Corresponding author: yoshida.yoshinori.p9@f.gifu-u.ac.jp

Abstract

Critical damage values in Cockcroft and Latham's and Ayada's models are automatically identified using autonomous-driven finite element analysis (ADFEA). To validate fracture behavior, we perform in-situ shearing and tensile tests on notched steel plates, employing image analysis. In ADFEA, a machine-learning-based optimization algorithm searches for critical damage values by minimizing the error between simulated and experimental fracture test results. The dependence of critical damage value identification on the testing method is demonstrated, and the effectiveness of fracture prediction theories and material testing methods for finite element analysis of shear processing is discussed.