Hybrid Artificial Intelligence Models with Multi Objective Optimization for Prediction of Tribological Behavior of Polytetrafluoroethylene Matrix Composites

Abstract

This study presents multi-response optimization and prediction tribological behaviors polytetrafluoroethylene (PTFE) matrix composites. For multi-response optimization, the Taguchi model was hybridized with grey relational analysis to produce grey relational grades (GRG). A support vector regression (SVR) model was combined with novel Harris Hawks’ optimization (HHO) and swarm particle optimization (PSO) models to form hybrid SVR–HHO and SVR–PSO models to predict the GRG. The prediction ability of the models was appraised using the coefficient of determination (R2), correlation coefficient (R), mean square error (MSE), root mean square (RMSE), and mean absolute percentage error (MAPE). The results of the multi-response optimization revealed that the optimal combination of parametric values of GRG for minimum tribological rate was 9 N-1000 mesh-0.14 ms−1-55 m (L3G1SD3SS3). An analysis of variance of the GRG showed that a grit size of 94.56% was the most significant parameter influencing the tribological behavior of PTFE matrix composites. The validation results revealed that an improvement of 52% in GRG was achieved. The prediction results of all models showed that the SVR–PSO and SVR–HHO models were superior to the SVR model. Furthermore, the SVR–HHO model produced superior prediction error and the best goodness of fit over the SVR–PSO model. These findings concluded that hybrids models are promising tools in the multi-response optimization and prediction of tribological behaviors of PTFE matrix composites. They can serve as a guide in the design and development of tribological materials.

Optimized machine learning with hyperparameter tuning and response surface methodology for predicting tribological performance in bio‐composite materials

“…Artificial neural networks constitute a category of machine learning models that draw inspiration from the structure and functionalities of the human brain. Comprising interconnected nodes, or artificial neurons, ANNs emulate the neural connections observed in biological systems 35,36 . The fundamental components of an ANN include the input layer, hidden layers, and the output layer.…”

“…Comprising interconnected nodes, or artificial neurons, ANNs emulate the neural connections observed in biological systems. 35,36 The fundamental components of an ANN include the input layer, hidden layers, and the output layer. Within this network, each connection between neurons is assigned a weight, a parameter subject to adjustment during the training phase to optimize the overall performance of the network.…”

Prediction of QCE using ANN and ANFIS for milling Alloy 2017A

“…During training, the network updates these prior distributions based on the observed data, resulting in posterior distributions that reflect the updated beliefs about the weights. Instead of providing a single prediction, BRANNs can generate a distribution of predictions, [27][28][29] which represents the uncertainty associated with each prediction. This uncertainty estimation can be valuable in decision-making processes where knowing the confidence or reliability of the predictions is crucial.…”