Optimization of hydroxyapatite powder mixed electric discharge machining process to improve modified surface features of 316L stainless steel. (June 2023)

• Record Type: Journal Article
• Authors: Danish, Mohd; Al-Amin, Md; Abdul-Rani, Ahmad Majdi; Rubaiee, Saeed; Ahmed, Anas; Zohura, Fatema Tuj; Ahmed, Rasel; Yildirim, Mehmet B
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 237:Number 3(2023)
• Page Start: 881
• Page End: 895
• Publication Date: 2023-06
• Subjects: Electro-discharge -- 316L steel -- process optimization -- recast layer -- roughness -- grey wolf optimizer; Mechanical engineering -- Periodicals; Production engineering -- Periodicals; Manufacturing processes -- Periodicals; 621.05
• DOI: 10.1177/09544089221111584
• Languages: English
• ISSNs: 0954-4089

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.