Evaluation of plastic properties and equi-biaxial residual stress via indentation and ANN

Abstract

This study presents a method for evaluating plastic properties and equi-biaxial residual stress (RS) simultaneously using the images of generated imprints in indentation tests and an artificial neural network (ANN). The ANN model is trained with data from finite element analysis (FEA) for establishing the relationship between material properties and the radial (ur) and vertical (uz) displacements resulting from indentation tests. To deal with the inherent noise in experimental data, an artificial random noise was added to the FEA data used for training the ANN model. By inputting parameters with artificial errors into the ANN model, the robustness of predicted values against potential testing errors was examined. Also, the accuracy of the predicted residual stresses and material properties from ANN are validated using tensile tests and indentation tests on the stress-induced specimens.

Keywords

• Plastic properties
• Residual stresses
• Indentation
• Finite element analysis (FEA)
• Digital image correlation (DIC)
• Artificial neural network (ANN)