Erosion rate of AA6082-T6 aluminum alloy subjected to erosive wear determined by the meta-heuristic (SCA) based ANFIS method

Abstract

Abstract AA6082-T6 aluminum alloy is used in different engineering applications. The erosive wear takes places in many machine parts. The prediction of wear amounts for aluminum alloy materials is complicated and nonlinear phenomena. The fuzzy inference systems (FIS) and the artificial neural networks (ANNs) have a series of properties on modeling nonlinear systems. In this study, it was aimed to determine the optimum erosive wear parameters in terms of wear resistance. This study suggests a meta-heuristic (sine–cosine algorithm-SCA) Based ANFIS prediction model for prediction of wear behavior of AA6082-T6 aluminum alloy within various impingement pressure, impact velocity, impingement angle and particle sizes. In this study, a model is developed that determines the optimum erosive wear parameters to achieve the minimum wear rate. The erosion rate-SCA Based ANFIS prediction model extracted reasonable results. Estimation capability has been achieved to 99.81 % by the proposed model.