Artificial neural networks-based [formula omitted]-integral prediction for cracked bodies under elasto-plastic deformation state –monotonic loading

Abstract

Artificial neural networks (ANNs) integrated with a finite element (FE)-based equivalent domain integral method are developed to compute J-integral at the vicinity of crack tips through a time-efficient approach. Robust ANN models are trained to establish nonlinear relationships between FE predicted elastic and elasto-plastic stress, strain, and displacement fields of stainless steel (SS304). Subsequently, elastic–plastic J-integral can be determined by using only elastic FE analysis solution rather than computationally expensive elasto-plastic FE analysis solution. The results show that well-trained ANN models can efficiently and accurately determine J-integral around the crack tips on the basis of numerical elastic FE analysis solution.