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    Optimizing of Wear Performance on Elevated Temperature of ZrO2 Reinforced AMCs Using Weighted Superposition Attraction Algorithm
    (Natl Inst Science Communication-Niscair, 2022) Simsek, Dogan; Ozyurek, Dursun; Ileri, Erol; Akpinar, Sener; Karaoglan, Deniz
    In the current study, the zirconium oxide (ZrO2) reinforced Aluminium Matrix Composites (AMCs) was designed as a brake lining and produced by mechanical alloying (MA) method. Wear tests of AMCs were performed according to ASTM G-99 at different sliding distance, operating temperatures and load in the range of 53 to 94 m, 20 to 340 degrees C and 10 to 30 N respectively. Optimum wear performance parameters were determined using the Weighted Superposition Attraction (WSA) algorithm. Firstly, to formulize the problem as an optimization problem through the guidance of the regression modelling, an experimental design has been constructed, and the wear tests have been done at different reinforced rates, operating temperature and loads. Secondly, WSA algorithm has been adapted to tackle the formulated optimization problem. According to the results of WSA algorithm, the optimum rate of zirkonium oxide (ZrO2), load and operating temperature was determined as 12%, 206.33 degrees C and 21.20 N respectively while keeping the friction coefficient between 0.15-0.24.
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    Optimizing tribological performance in elevated temperature of TiC reinforced A356 matrix composites designed as automotive friction materials using dragonfly algorithm
    (Sage Publications Ltd, 2023) Simsek, Dogan; Ozyurek, Dursun; Ileri, Erol; Karaoglan, Aslan Deniz
    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 degrees C, and 20 N respectively to optimize the responses, namely weight loss, wear rate, and friction coefficient.