Optimization of wear parameters for ECAP-processed ZK30 alloy using response surface and machine learning approaches: a comparative study.

ABSTRACT: The present research applies different statistical analysis and machine learning (ML) approaches to predict and optimize the processing parameters on the wear behavior of ZK30 alloy processed through equal channel angular pressing (ECAP) technique. Firstly, The ECAPed ZK30 billets have been examined at as-annealed (AA), 1-pass, and 4-passes of route Bc (4Bc). Then, the wear output responses in terms of volume loss (VL) and coefficient of friction (COF) have been experimentally investigated by varying load pressure (P) and speed (V) using design of experiments (DOE). In the second step, statistical analysis of variance (ANOVA), 3D response surface plots, and ML have been employed to predict the output responses. Subsequently, genetic algorithm (GA), hybrid DOE-GA, and multi-objective geneti

SUBMITTER: Shaban M 

PROVIDER: S-EPMC11576740 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature