Optimizations of Process Parameter for Erosion Wear Using Sustainable Machine Learning Approach

Abstract

Aim of current study is to utilize different sustainable artificial intelligence (AI) tools to check the influence of test factors on erosion wear. Bottom ash is taken as erodent at different solid concentration while brass is considered as base material. The parameters involved are rotational speed (N), solid concentration (CW), and testing time duration (T). According to experimental results and analysis based on different AI tools , it is abundantly found that erosion wear have a significant dependency on parameters such as N, CW, T and the order of maximum erosion was found as N > CW >T. The rate of rotation speed (N) has been identified as the factor that has the greatest impact on the degree to which erosion wear occur. 3D analysis has been conducted for the maximum and minimum erosion wear condition. In order to verify the accuracy, four distinct methods are utilized; nonetheless, the accuracy of the regression analysis has been found more promising when compared to that of the Ridge, lasso and neural network methodologies.