Data-driven artificial neural network for elastic plastic stress and strain computation for notched bodies

•A new modeling framework is developed to compute elastic–plastic stresses and strains fields for notched bodies.•The proposed approach is based on the integration of artificial neural network (ANN) and finite element (FE) analysis.•The approach predicts elastic–plastic stress and strain fields of notched bodies using a linear elastic FE analysis solution.•The proposed modeling approach provides efficient and accurate predictions. An integration of artificial neural network (ANN) and finite element (FE) analysis was developed to predict elastic–plastic stress and strains at notch locations based on a linear elastic FE analysis solution. Two FE models were created for different materials and multiaxial load cases to produce hypothetical elastic and elastic–plastic stress and strain datasets. One model was based on an elastic state, and the other was based on an elastic–plastic state. The ANN was trained with the elastic stress data from the linear elastic FE model as input and the elastic–plastic stress–strain data from the nonlinear elastic–plastic FE model as output. The dataset was divided into three groups: training, verification, and testing data. The ANN was trained using the training data, evaluated using the verification data, and tested for generalizability using the testing data. The results showed that the proposed methodology can predict elastic–plastic stress and strain fields for notched bodies under multiaxial loadings accurately and efficiently using only the elastic FE analysis solution.