Optimization of Magnetic Abrasive Finished Aluminum 2024 alloy Using Jaya Algorithm, Genetic Algorithm and Grey Relational Analysis

EasyChair Preprint no. 2269

Abstract

Magnetic Abrasive Finishing (MAF) is one of the advanced finishing processes that produce a surface finish at the nano scale for magnetic and non-magnetic materials. The surface finish of the material can be enhanced significantly by optimizing the major process parameters of MAF process. The present paper investigates the optimization of MAF process parameters. Grey relational analysis (GRA), Genetic algorithm (GA) and the Jaya Algorithm (JA) were used and compared to analyze the best optimum solution for MAF process parameters while processing aluminum 2024 alloy (Al 2024) plate. The process parameters such as voltage (V), speed of the electromagnet (RPM) and weight ratio of abrasives (%) were considered as input variables, whereas percentage improvement of surface finish (%ΔR_a) was considered as response. Based on the literature and trial experiments, the range of each process parameters were decided and L9 orthogonal array was designed. The analysis of variances (ANOVA) analysis and regression equation were obtained with the help of Minitab17 software. The regression equation was used to get the optimum set of process parameters using GRA, GA and JA algorithms. These optimum results were compared and JA was shown to be the best optimization technique, which gave the best optimum solution.

Keyphrases: Aluminum 2024 alloy, Genetic Algorithm, Grey Relational Analysis, Jaya algorithm, Magnetic Abrasive Finishing, Surface finish

@Booklet{EasyChair:2269,
  author = {Kamepalli Anjaneyulu and Gudipadu Venkatesh},
  title = {Optimization of Magnetic Abrasive Finished Aluminum 2024 alloy Using Jaya Algorithm, Genetic Algorithm and Grey Relational Analysis},
  howpublished = {EasyChair Preprint no. 2269},

  year = {EasyChair, 2019}}