Logo

Multi-objective optimization of process variables for MWCNT-added electro-discharge machining of 316L steel

Al-Amin, M. and Abdul-Rani, A.M. and Ahmed, R. and Shahid, M.U. and Zohura, F.T. and Rani, M.D.B.A. (2021) Multi-objective optimization of process variables for MWCNT-added electro-discharge machining of 316L steel. International Journal of Advanced Manufacturing Technology, 115 (1-2). pp. 179-198.

Full text not available from this repository.

Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Fabrication of 316L steel with the desired surface integrity and thin modified recast layer for manufacturing various devices is very challenging using the traditional techniques and requires post-processing. Nevertheless, electro-discharge machining (EDM) is an evolving candidate among the non-traditional processes offering concurrent machining and surface alteration for the instrument manufacturing industry. To explore its full potential, this research work provides a thorough investigation of process variables on the machining performances and surface features primarily required for processing 316L steel in the industry. In this study, nano multi-walled carbon nanotubes (MWCNT) are utilized to improve the machining and surface responses. In addition to this, the parametric optimization is conducted through a Taguchi-based design which assists to obtain the highest material removal rate (MRR) of 42.25 mg/min corresponding to a 10-A peak current, 16-μs pulse-on time, 1-g/l MWCNT amount, and 45 duty cycle while the lowest surface roughness (SR) and recast layer thickness (RLT) of 1.58 μm and 5.243 μm respectively are attained at a 5-A peak current, 8-μs pulse-on time, 0.7-g/l MWCNT amount, and 45 duty cycle. Analysis of variance (ANOVA) reports peak current being the most momentous parameter complied by the MWCNT amount, pulse-on time, and duty cycle for MRR, SR, and RLT. The best 21 solution sets predicted through the multi-objective optimization tool called non-dominated sorting genetic algorithm-II (NSGA-II) obeying the set objective functions are proposed which are obtained from the Pareto optimal frontiers. Accuracy levels of the predicted solution sets are verified by the confirmatory experiments showing the estimated errors of less than 10. SEM analyses confirm excellent surface integrity with a comparatively thin recast layer formation. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

Item Type:Article
Impact Factor:cited By 1
Uncontrolled Keywords:Analysis of variance (ANOVA); Austenitic stainless steel; Genetic algorithms; Manufacture; Multiobjective optimization; Pareto principle; Surface roughness, Electro discharge machining; Instrument manufacturing; Machining performance; Material removal rate; Non dominated sorting genetic algorithm ii (NSGA II); Parametric optimization; Pareto-optimal frontiers; Traditional techniques, Multiwalled carbon nanotubes (MWCN)
ID Code:23816
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:19 Aug 2021 13:09
Last Modified:19 Aug 2021 13:09

Repository Staff Only: item control page