Prediction of fracture toughness using artificial neural networks (ANNs)

This paper explores the potential use of artificial neural networks (ANNs) in the field of fracture mechanics. It addresses the use of ANNs in predicting the average fracture toughness, (K) over bar(c), of 7075-T651 aluminum alloy under uni- as well as biaxial loading at the room and higher temperatures. The fracture toughness prediction is based on the evaluation of critical values of J from experimental data. Parameters that influence the value of the fracture toughness are used to develop the ANN model, and the contribution of these variables to the variation of fracture toughness is then found. This paper also explores the effect of crack geometry, temperature and biaxiality on fracture toughness. Results indicate that ANN predicted the fracture toughness under different conditions with high accuracy. It also demonstrates that ANN is an excellent analytical tool that, if properly used, can reduce cost, time and enhance structure reliability. Copyright (C) 1996 Elsevier Science Ltd