Prediction on Tribology Behavior of PEEK Composites Using Back Propagation Artificial Neural Networks

A multi-layer back propagation artificial neural network (BPNN) was used to predict the friction coefficient and the specific wear rate of short fiber reinforced polyetheretherketone (PEEK) composites. The best network structure for property forecast is decided as 5-[29]₁-2. The train function is trainlm, and the transfer functions from input to hidden layers and from hidden to output layers are logsig and purelin, respectively. For the network of ingredient forecast, the best network structure is 2-[300]₁-[150]₂-4. The train function is trainscg, and logsig, tansig are the transfer functions from input to hidden layers and within the hidden layers, respectively. Purelin is transfer function between hidden and output layers. The results show that ANN techniques can effectively be used to predict the tribology behavior and the components of composites.