Artificial neural network predictions on erosive wear of polymers

In the present paper, an artificial neural network (ANN) approach was applied to the erosive wear data of three polymers, i.e. polyethylene (PE), polyurethane (PUR), and an epoxy modified by hygrothermally decomposed polyurethane (EP-PUR). Three independent datasets of erosive wear measurements and characteristic properties of these polymers were used to train and test the neural networks. For the first two material examples, the impact angle of solid particle erosion and some characteristic properties were selected as ANN input variables. Whereas the third one, material compositions, i.e. epoxy and HD-PUR weight contents, were also involved as additional ANN input variables. In all these cases, the output parameter was the erosive wear rate. Acceptable ANN predictive qualities were reached, demonstrating that ca. 35–80% of the randomly selected test dataset had a coefficient of determination B≥0.9 for these three cases, respectively. Ranking of the importance of characteristic properties to erosive wear rate could offer some information about which property has a stronger relationship to wear in each polymer case. Even though the ANN approach is only a phenomenological method, a well-trained ANN is believed to be also of help for a mechanistic understanding of the problem considered.