Wear volume prediction with artificial neural networks

Abstract

In this study, the potential of artificial neural network techniques to predict and analyze the wear behaviour of short fibre reinforced polymeric bearing materials was investigated. Artificial neural networks have been recently introduced into tribology by Jones et al. (1997) [Jones, Jansen and Fusaro, Preliminary investigation of neural network techniques to predict tribological properties. Tribology Transactions 1997;40(2):312]. Their work is extended here in three directions: 1. A higher number of input variables characterizing the materials and the experimental conditions is used (10 instead of 6). Based on a principal component analysis, correlations in the input vector are identified and used to reduce its dimensionality. 2. A statistical technique is used to improve the predictive capabilities of the artificial neural network (Bayesian regularization instead of early stopping used by Jones et al.). This technique also automatically identifies the optimal size of the artificial neural network, which was determined experimentally by the other group of authors. 3. The quality of the predictions is evaluated. While Jones et al. found values well above 0.9 for the coefficient of determination on a fixed set of test data, it is shown here based on a dataset which was obtained at the IVW in fretting experiments for different composites that the average results are not so good if a large number of randomly chosen test data sets is considered. However, it is argued that the results are still reasonable when compared with the substantial wear volume measurement error. Also, improved results can be expected in the future from a further optimization of the network construction as well as from an increasing availability of measurement data that can be used to train the network.