Specific Wear Rate Modeling of Polytetraflouroethylene Composites via Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) Tools

Yusuf Şahin,Musa Alhaji Ibrahim,Mukhtar Nuhu Yahya,Auwalu Yusuf Gidado,Auwal Ibrahim

doi:10.5772/intechopen.95242

Abstract

Lately, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models have been recognized as potential and good tools for mathematical modeling of complex and nonlinear behavior of specific wear rate (SWR) of composite materials. In this study, modeling and prediction of specific wear rate of polytetraflouroethylene (PTFE) composites using FFNN and ANFIS models were examined. The performances of the models were compared with conventional multilinear regression (MLR) model. To establish the proper choice of input variables, a sensitivity analysis was performed to determine the most influential parameter on the SWR. The modeling and prediction performance results showed that FFNN and ANFIS models outperformed that of the MLR model by 45.36% and 45.80%, respectively. The sensitivity analysis findings revealed that the volume fraction of reinforcement and density of the composites and sliding distance were the most and more influential parameters, respectively. The goodness of fit of the ANN and ANFIS models was further checked using t-test at 5% level of significance and the results proved that ANN and ANFIS models are powerful and efficient tools in dealing with complex and nonlinear behavior of SWR of the PTFE composites.

Highlights

In the study of tribology, highly nonlinear and very complex relationship exists
feed forward neural network (FFNN) and adaptive neuro fuzzy inference system (ANFIS) models were found to be highly qualitative in predicting the specific wear rate (SWR) of the composites, yet multilinear regression (MLR) model was found to be incapable in the same prediction scenario
The high prediction performance of the FFNN and ANFIS models is owing to their capability to deal with nonlinear, noisy and complex relationship which is typical of SWR of the polymer composites

Summary

Introduction

Specific wear rate of materials especially polymer matrix composites emanates from scores of intricate associations on both microscopic and macroscopic levels between surfaces which are in contact [1]. These associations depend upon tribological, Virtual Assistant geometrical as well as material behaviors of the contacting surfaces and the sliding conditions for example, temperature, type of contact, lubricating conditions, applied load, etc. Simulation of tribological properties usually deals with building of mathematical models extracted from practical data. The numbers of these models were obtained to simulate specific wear rate of materials under restricted conditions. No distinctive model was universalized to reveal the specific wear rate of polymer matrix composites

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Conclusion