Liping Zu¹, Yaxun Liu¹, Haoran Zhang¹, Lisheng Liu^2,*, Xin Lai^2,*, Hai Mei²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2349-2371, 2024, DOI:10.32604/cmes.2024.050488

Abstract Fracture in ductile materials often occurs in conjunction with plastic deformation. However, in the bond-based peridynamic (BB-PD) theory, the classic mechanical stress is not defined inherently. This makes it difficult to describe plasticity directly using the classical plastic theory. To address the above issue, a unified bond-based peridynamics model was proposed as an effective tool to solve elastoplastic fracture problems. Compared to the existing models, the proposed model directly describes the elastoplastic theory at the bond level without the need for additional calculation means. The results obtained in the context of this model are shown More >

Kyungsuk Jang¹, Gun Jin Yun^2,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1091-1120, 2021, DOI:10.32604/cmc.2020.012911

Abstract This paper first proposes a new self-learning data-driven methodology that can develop the failure criteria of unknown anisotropic ductile materials from the minimal number of experimental tests. Establishing failure criteria of anisotropic ductile materials requires time-consuming tests and manual data evaluation. The proposed method can overcome such practical challenges. The methodology is formalized by combining four ideas: 1) The deep learning neural network (DLNN)-based material constitutive model, 2) Self-learning inverse finite element (SELIFE) simulation, 3) Algorithmic identification of failure points from the self-learned stress-strain curves and 4) Derivation of the failure criteria through symbolic regression More >