Application of artificial neural network for predicting plain strain fracture toughness using tensile test results

A back-propagation neural network was applied to predicting the K-IC values using tensile material data and investigating the effects of crack plane orientation and temperature. The 595 K-IC data of structural steels were used for training and testing the neural network model. In the trained neural network model, yield stress has relatively the most effect on K-IC value among tensile material properties and K-IC value was more sensitive to K-IC test temperature than to crack plane orientation valid in the range of material data covered in this study. The performance of the trained artificial neural network (ANN) was evaluated by comparing output of the ANN with results of a conventional least squares fit to an assumed shape. The conventional linear or nonlinear least squares fitting methods gave very poor fitting results but the results predicted by the trained neural network were considerably satisfactory. This study shows that the neural network can be a good tool to predict K-IC values according to the variation of the temperature and the crack plane orientation using tensile test results.