Prediction of wear behaviors of nickel free stainless steel–hydroxyapatite bio-composites using artificial neural network

This paper presents the development of a back propagation neural network model in order to predict the wear behaviors of nickel free stainless steel–hydroxyapatite bio-composites which have been tested in air and ringer solution. The model is based on experimental results related to wear tests of composites with various hydroxyapatite contents at different wear loads and distances. The wear load, HA volume fraction, and wear distance have been considered as the input parameters and the wear volume loss as the output parameter. Then the predicted results were compared with experimental results and it was found out that the results obtained from neural network model were accurate in predicting the wear volume loss. The results showed that artificial neural network (ANN) is an effective tool in the prediction of produced composites properties and quite useful rather than using time-consuming experimental processes. Since predicted data by neural network model were so similar to the experimental results, we used this model to determine the volume loss of different composites for wear distance in range of 0–1000 m and different applied wear loads.