Professor of Mechanical Engineering and Smart Structures, School of Computing Engineering and Mathematics, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—Modeling frictional performance of a break pad material is difficult and requires the use of complex numerical models. The current work utilizes one of the Artificial Intelligence techniques, Least Squares Support Vector Machine (LS-SVM), to model the nonlinear relationships between the input breaking conditions and the frictional and thermal performance of previously developed non-commercial brake pad materials. Experimental data were produced and used in training and testing the proposed LS-SVM models. The results indicate that LS-SVM constitutes a robust methodology and the proposed models could be used to predict the friction coefficients and the induced interface temperature of brake pad materials in order to reduce experimental time and cost.
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