Artificial neural networks for predicting sliding friction and wear properties of polyphenylene sulfide composites |
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Authors: | Lada A Gyurova Klaus Friedrich |
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Affiliation: | a Institute for Composite Materials (IVW GmbH), Technical University of Kaiserslautern, Erwin Schrödinger Straße 58, 67663 Kaiserslautern, Germany b CEREM, College of Engineering, King Saud University, 11421 Riyadh, Saudi Arabia |
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Abstract: | In this paper the potential of using artificial neural networks (ANNs) for the prediction of sliding friction and wear properties of polymer composites was explored using a newly measured dataset of 124 independent pin-on-disk sliding wear tests of polyphenylene sulfide (PPS) matrix composites. The ANN prediction profiles for the characteristic tribological properties exhibited very good agreement with the measured results demonstrating that a well trained network had been created. The data from an independent validation test series indicated that the trained neural network possessed enough generalization capability to predict input data that were different from the original training dataset. |
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Keywords: | Polymer composite Friction Wear Artificial neural network |
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