Artificial neural network accomplished prediction on tribology – A promising procedure to facilitate the tribological characterization of polymer composites

Abstract

The aim of our investigations was to develop an artificial neural network (ANN) to reliably describe the pv dependence of different hybrid materials with identical basic components but different quantity ratios, and ultimately to explore whether it is possible to predict the effect of different component ratios on the tribological behavior on the basis of a limited number of experiments. For this purpose, the pressure (p) and velocity (v) dependence for five PEEK-based compounds systematically modified in their composition was investigated at different load levels on a block-on-ring tribometer. In parallel, an ANN was designed and optimized using Bayesian optimization. The results of four out of this five materials was used to train a ANN, whereas the Levenberg-Marquardt training algorithm was used as a kernel. This allowed us to describe the pv dependence of friction and wear of these materials in a fine-grained manner and thus evaluate their suitability under different operating conditions. The procedure was performed twice with four different materials.The experimental results of the fifth material in each case, which was not used for training, show very good agreement with the friction and wear properties predicted by the artificial neural network. It is thus shown that the combination of comparatively few experiments and ANN simulations enables a reliable and fine-mesh prediction of the friction and wear behavior of materials in a wide pv load range. Ultimately, the proportions of material components for different pv load ranges can thus be optimized by simulation, which would not be possible in experiments due to time and cost constraints. The comparative evaluation of different materials can be illustrated clearly in so-called tribomaps.