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Optimization of the Wire Electric Discharge Machining Process of Nitinol-60 Shape Memory Alloy Using Taguchi-Pareto Design of Experiments, Grey-Wolf Analysis, and Desirability Function Analysis Wasiu Oyediran Adedeji; Mofoluwaso Kehinde Adeniran; Kasali Aderinmoye Adedeji; John Rajan; Sunday Ayoola Oke; Elkanah Olaosebikan Oyetunji
IJIEM - Indonesian Journal of Industrial Engineering and Management Vol 4, No 1: February 2023
Publisher : Program Pascasarjana Magister Teknik Industri Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijiem.v4i1.18087

Abstract

The nitinol-60 shape memory alloy has been rated as the most widely utilized material in real-life industrial applications, including biomedical appliances, coupling and sealing elements, and activators, among others. However, less is known about its optimization characteristics while taking advantage to choose the best parameter in a surface integrity analysis using the wire EDM process. In this research, the authors proposed a robust Taguchi-Pareto (TP)-grey wolf optimization (GWO)-desirability function analysis (DFA) scheme that hybridizes the TP method, GWO approach, and DFA method. The point of coupling of the TP method to the GWO is the introduction of the discriminated signal-to-noise ratios contained in the selected 80-20 Pareto rule of the TP method into the objective function of the GWO, which was converted from multiple responses to a single response accommodated by the GWO. The comparative results of five outputs of the wire EDM process before and after optimization reveals the following understanding. For the CR, a gain of 398% was observed whereas for the outputs named Rz, Rt, SCD, and RLT, losses of 0.0996, 0.0875, 0.0821, and 0.0332 were recorded. This discrimination of signal-to-noise ratio based on the 80-20 rule makes the research different from previous studies, restricting the data fed into the GWO scheme to the most essential to accomplishing the TP-GWO-DFA scheme proposed. The use of the TP-GWO-DFA method is efficient given the limited volume of data required to optimize the wire EDM process parameters of nitinol.
Exploiting Tournament Selection-Based Genetic Algorithm in Integrated AHP-Taguchi Analyses-GA Method for Wire Electrical Discharge Machining of AZ91 Magnesium Alloy Sunday Ayoola Oke; Wasiu Oyediran Adedeji; Meshach Chukwuebuka Ikedue; John Rajan
IJIEM - Indonesian Journal of Industrial Engineering and Management Vol 4, No 1: February 2023
Publisher : Program Pascasarjana Magister Teknik Industri Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijiem.v4i1.17387

Abstract

Concurrent optimization and prioritization of wire EDM parameters can improve resource allocations in material processing and should be effective. This study advances the integrated analytic (AHP)-Taguchi(T)-tournament-based-genetic algorithm (tGA) method to moderate the influence of erroneous resource allocation in parametric analysis decisions in wire electrical discharge machining. The structure builds on the AHP-T method’s platform obtained from the literature and develops it by including the tGA while processing the AZ91 magnesium alloy. The article evaluates the delta values for the average signal-to-noise ratios in the response table and deploys them to arrive at the winners in a league and consequently mutate the chromosomes for performance improvement. The scale of relative importance, consistency index, optimal parametric setting, delta values, and ranks are all established and coupled with the total value and maximum value evaluation at the selection crossover and mutation stages of the genetic algorithm. The results at the mutation, crossover, and selection stages of the tournament selection process showed total values of 124410, 96650, and 70564, respectively. At the selection stage, the maximum value to be the winner of the tournament is 28704. The crossover operation was accomplished after the 5th, 5th, and 6th bit for the first three pairs, respectively. For the selection and crossover operations, the maximum value is 28604 and 27944, respectively. The research clarifies which parameters are the best and worst during optimization using the AHP-T-tGA method.