Using ANN and OA techniques to determine the specific wear rate effectors of A356 Al-Si/Al2O3 MMC

Abstract

In the present work, it’s required to obtained the wear rate effectors’ values of A356 Al-Si/Al2O3 composite (Al2O3 wt%, applied load, hardness, and sliding distance) required to obtain a certain specific wear rate. So, the specific wear behavior of cast and heat-treated A356 Al-Si/Al2O3 metal matrix composites (MMC) were investigated as a function of its effectors. Five weight fractions of Al2O3 particles were used to produce specimens using stir casting. Two different hardness are obtained for each fraction (casted and heat-treated specimens). Sliding wear tests were done under three loads (20, 30, and 40 N), four sliding distances (310, 620, 930 and 1240 m) at constant speed (1 m/min). Experimental results indicated that the specific wear rate is generally reversed proportional to Al2O3 percentage. The impact of Al2O3 percentage was eliminated with the grown of applied load. Increasing the applied load decreases the specific wear rate, up to 20% Al2O3. However, at 25% Al2O3 the applied load increases the specific wear rate with a small graduation. Moreover, the heat treatment process improves the hardness and specific wear behavior of the casted MMC. Both Artificial neural network (ANN) and multiple regression techniques were used to predict the wear rate. The orthogonal array technique (OA) used in selecting the data set to train ANN and obtained a 2nd degree regression equation. ANN gives more realistic prediction then the regression equation. So, at the end, an algorithm is designed and tested to determine the weight fraction and other wear rate effectors for A356 Al-Si/Al2O3 MMC to obtain a certain wear rate, according to the uncertainty of the ANN. The used algorithm for obtaining the wear rate effectors provides a very good choices to produce a certain wear rate’s value, with error less than 1%.