PREDICTING THE MASS LOSS OF POLYTETRAFLOUROETHYLENE-FILLED COMPOSITES USING ARTIFICIAL INTELLIGENCE TECHNIQUES
Publication Date : 19/01/2021
Mass loss of composite materials plays a key role in most of the industries. The physical experimentation of quantifying the wear is costly and time-consuming. It is therefore important to put forward predictive techniques which can predict and evaluate the mass loss of composite materials. In the current time, artificial intelligence-based techniques like multi-layered perceptron (MLP), adaptive neuro fuzzy inference system (ANFIS) are mostly recognized as tools of prediction. In this study, prediction of mass loss of polytetraflouroethylene filled with glass, carbon and bronze fibres was examined. The prediction results showed that MLP and ANFIS techniques outperformed MLR technique by 45.36% and 45.80%, respectively. ANFIS and MLP techniques exhibited good agreement between the predicted and the observed values of the PTEE-filled composites mass loss. The AI techniques proved to be robust tools in predicting the mass loss of the PTFE-filled composites.
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