Optimization of hydroxyapatite powder mixed electric discharge machining process to improve modified surface features of 316L stainless steel

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Authors
Danish, Mohd
Al-Amin, Md
Abdul-Rani, Ahmad Majdi
Rubaiee, Saeed
Ahmed, Anas
Zohura, Fatema Tuj
Ahmed, Rasel
Yildirim, Mehmet Bayram
Advisors
Issue Date
2022-07-12
Type
Article
Keywords
Electro-discharge , 316L steel , Process optimization , Recast layer , Roughness , Grey wolf optimizer
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Organizational Units
Journal Issue
Citation
Danish, M., Al-Amin, M., Abdul-Rani, A. M., Rubaiee, S., Ahmed, A., Zohura, F. T., Ahmed, R., & Yildirim, M. B. (2022). Optimization of hydroxyapatite powder mixed electric discharge machining process to improve modified surface features of 316L stainless steel. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering. https://doi.org/10.1177/09544089221111584
Abstract

Traditional methods for producing 316L steel with a desired roughness and a uniform thin coating for sufficient bioactivity and long-term durability are highly difficult and require post-processing. Electric discharge machining (EDM) is a nontraditional process that can accomplish both machining and coating the surface concurrently. This study provides a thorough examination of the impacts of EDM process parameters on the machining responses, which are significantly necessary for processing 316L steel to reach its full potential. The nano-hydroxyapatite particles added EDM method is used to improve the machining and the surface responses. EDM process parametric optimization is conducted using Taguchi's design to achieve the lowest surface roughness of 2.04?µm and a recast layer thickness of 6.11?µm. As a machinability metric, the greatest material erosion rate (MER) of 20.64?mg/min is achieved. Current intensity, discharge period and hydroxyapatite amount are identified to be important predictors for MER, surface roughness and recast layer thickness. Scanning electron microscope with the energy dispersive X-ray spectrums and atomic force microscopic images validate the machined surface morphology, elemental compositions and surface topography. Errors of the confirmatory tests are less than 10%, showing that the projected solution sets by the grey wolf optimizer are highly accurate. The study shows a thin recast layer with both nano and micro-surface roughness formation, which have a great importance in biomedical applications.

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Publisher
SAGE Journals
Journal
Book Title
Series
Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering
2022
PubMed ID
DOI
ISSN
0954-4089
EISSN