This study presents optimization and prediction of tribological behaviour of filled polytetrafluoroethylene (PTFE) composites using hybrid Taguchi and support vector regression (SVR) models. To achieve the optimization, Taguchi Deng was employed considering multiple responses and process parameters relevant to the tribological behaviour. Coefficient of friction (µ) and specific wear rate (Ks) were measured using pin-on-disc tribometer. In this study, load, grit size, distance and speed were the process parameters. An L27 orthogonal array was applied for the Taguchi experimental design. A set of optimal parameters were obtained using the Deng approach for multiple responses of µ and KS. Analysis of variance was performed to study the effect of individual parameters on the multiple responses. To predict µ and Ks, SVR was coupled with novel Harris Hawks’ optimization (HHO) and swarm particle optimization (PSO) forming SVR-HHO and SVR-PSO models respectively, were employed. Four model evaluation metrics were used to appraise the prediction accuracy of the models. Validation results revealed enhancement under optimal test conditions. Hybrid SVR models indicated superior prediction accuracy to single SVR model. Furthermore, SVR-HHO outperformed SVR-PSO model. It was found that Taguchi Deng, SVR-PSO and SVR-HHO models led to optimization and prediction with low cost and superior accuracy.

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.

Polymer-based composites find applications in several areas because of their exceptional properties. This article deals with Taguchi grey relational optimization method of abrasive parameters (load (L), grit size (G) and sliding distance (D)) and their influence on abrasive performance of reinforced polytetrafluoroethylene (PTFE) composites. A Taguchi L9 orthogonal array was designed, and nine experimental tests were conducted based on the Taguchi designed experiments. A pin-on-disc tribology machine was used for the experiments. The coefficient of friction (µ) and abrasive specific wear rate (Aw) were recorded for each experiment. An analysis of variance (ANOVA) was performed to establish the significance and percentage contribution of each parameter affecting the abrasive wear performance. Results from the Taguchi-grey-relational method showed that the optimal combination of parameters was achieved at load of 10 N, grit size of 1000 mesh, and sliding distance of 350 m (coded as L3G1D3). ANOVA findings revealed that a grit size with 67.69 % as the most influential on the abrasive performance of polymer-based composites. Validation tests performed using the optimal combination parameter showed an enhancement of 55.22 % in grey relational grade.

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Filled polymer composites are widely used for automobile, structural and aerospace components owing to their exceptional combination of high specific stiffness and strength. This study presents Taguchi-Deng optimization of tribological parameters such as load, grit size, distance and speed as well as prediction of tribological behaviours of carbon-filled and bronze-filled polytetrafluoroethylene (PTFE) composites using pin on disk configuration. A plan of experiments based on Taguchi L27 (43) orthogonal array (OA) was designed to collect data in a controlled manner. The Taguchi L27 (43) was hybridized with Deng model to produce grey relational grades (GRG) for the multiple response optimization. Analysis of variance (ANOVA) was executed to establish the parameters affecting GRG of the composites. For the prediction of the tribological behaviours of the composites namely coefficient of friction (µ) and specific wear rate (Ks), support vector regression (SVR) was coupled with novel Harris Hawks’ optimization (HHO) and swarm particle optimization (PSO) forming SVR-HHO and SVR-PSO models respectively, were employed. Prediction accuracy of the models were appraised using coefficient of determination (R2), correlation coefficient (R), root mean square error (RMSE) and mean absolute percentage error (MAPE). GRG results revealed that optimum parameters which reduced tribological behaviours were factor combination L3G1D3S3. ANOVA for GRG reveled that grit size with 68.57% ranks as the most influential parameter followed by load with 20.57%, followed by distance having a contribution of 7.78% and finally speed with least contribution of 3.38% for minimum tribological loss. Validation performed using optimum parameters revealed an enhancement of 55% in GRG. Prediction accuracy of the single model increase to 19.50% and 57.08% on the average for hybrid µ and Ks models, respectively. Furthermore, SVR-HHO model indicated the higher prediction accuracy of the tribological behaviours of filled PTFE composites as compared to SVR-PSO model. These findings concluded these metaheuristic models are promising in predicting tribological behaviours of filled PTFE composites and thus can serve as a guide in the design and development of tribological materials.