
Novel EDAS-Taguchi and EDAS-Taguchi-Pareto Methods for Wire EDM Process Parametric Selection of Ni55.8Ti (Nitinol) Shape Memory Alloy
Author(s) -
Kenechukwu Obinna Okponyia,
Sunday Ayoola Oke
Publication year - 2021
Publication title -
international journal of industrial engineering and engineering management
Language(s) - English
Resource type - Journals
ISSN - 2685-4090
DOI - 10.24002/ijieem.v3i2.4998
Subject(s) - edas , taguchi methods , multi objective optimization , pareto principle , computer science , orthogonal array , mathematical optimization , mathematics , estimation of distribution algorithm , machine learning
The EDAS (evaluation based on distance from average solution) method is a broadly utilized tool for multi-criteria analysis with the ability to handle several conflicting criteria. The Taguchi method is an optimization tool with economic capability in experimentation. This article presents EDAS Taguchi (EDAS-T) method based on EDAS and the Taguchi method. It also presents EDAS Taguchi-Pareto (EDAS-TP) method framed from EDAS and Taguchi-Pareto methods. Furthermore, data from the literature to test the proposed methods are presented, which the results are compared. This research shows that the EDAS method produces the optimum combination of parameters at a run with a current of 4A, pulse on time of 50 µs, pulse off time of 14ms, and powder concentration of 1 g/L. Also, the EDAS-Taguchi method reveals a current of 4A, pulse on time of 60 µs, pulse off time of 14 µs, and powder concentration of 1 g/L. However, the principal result is that using the EDAS Taguchi-Pareto method, the optimal current is 3A, pulse on time is 60 µs, and powder concentration is 0.75g/L. The EDAS Taguchi-Pareto method eliminated the pulse off time and pulse on time, claiming that it is not significant to the system's optimum performance. The principal novelty of this article is that it introduces a mechanism of concurrently optimizing and selecting the wire EDM process parameters using the EDAS-Taguchi-Pareto method. The optimization is parallelly conducted as selection occurs, providing an initial notification to ascertain timely detection and control of local optimality of parameters to global optimization before final selection. This is unlike most evaluations, where optimization is done differently from the selection. This study is the first to develop and use EDAS methods for the WEDM process of Ni55.8Ti shape memory alloy.