Optimizing tribological performance in elevated temperature of TiC reinforced A356 matrix composites designed as automotive friction materials using dragonfly algorithm
Restricted accessResearch articleFirst published online February, 2023
Optimizing tribological performance in elevated temperature of TiC reinforced A356 matrix composites designed as automotive friction materials using dragonfly algorithm
The objective of this study designed in three steps is to evaluate the optimization of tribological performance in the elevated temperature of TiC reinforced A356 matrix composites, produced by mechanical alloying (MA), using the dragonfly algorithm (DA). In the first step, an experimental design is created to formulate the problem as an optimization problem with the help of regression modeling, and wear tests by adding a temperature module according to ASTM G99-05 standards are performed at various reinforced rates, operating temperatures, and loads. In the second step, regression models are fitted to the experimental results. In the third step, the dragonfly algorithm (DA) – one of the recent nature inspired metaheuristic optimization algorithms – has been adapted to optimize the formulated problem. The results of DA indicate that the optimum factor levels for the reinforced rate, operating temperature, and load are determined as 7%, 10°C, and 20 N respectively to optimize the responses, namely weight loss, wear rate, and friction coefficient.
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