Multi-objective optimization of process variables for MWCNT-added electro-discharge machining of 316L steel - Al-Amin, M. (Springer Science and Business Media Deutschland GmbH, 2021)
Fabrication of 316L steel with the desired surface integrity and thin modified recast layer for manufacturing various devices is very challenging usingÂ 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 processingÂ 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-Î¼s 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 Î¼m and 5.243 Î¼m respectively are attained at a 5-A peak current, 8-Î¼s 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 layerÂ formation. Â© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.