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

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.

Investigation of enhancing EDM machining performance of INCONEL alloy using composite electrodes

“…The influence of various process parameter on the TWR was depicted in the figure 3. The TWR increases with raise in current, generated spark removes material from the work piece as well as tool material [33]. At higher current owing to the high intensity, it erodes high quantity of materials from the electrodes, results in increase in TWR.…”

Parameters Optimization of Electrical Discharge Machining Process Using Swarm Intelligence: A Review

“…Rouniyar and Shandilya [69] performed the optimization of process parameters in magnetic field assisted powder mixed EDM (MFAPM-EDM) for cutting Al 6061, and compared the optimized performance of GA method with others. Danish et al [70] used the GWO approach to optimize the hydroxyapatite powder mixed electric discharge machining (HPM-EDM) process in order to improve modified surface characteristics, such as the recast layer thickness (RLT), of 316L stainless steel. The test demonstrated that the gray wolf optimizer's predicted solution sets are very accurate, with less than 10% inaccuracy.…”

Machine Learning Based Predictive Modeling of Electrical Discharge Machining of Cryo-Treated NiTi, NiCu and BeCu Alloys

“…Electrical discharge machining (EDM) is one of the practical solution methods for machining hard materials [1][2][3][4][5]. The complexity of EDM process led the researchers to take a lot of effort for finding the optimum process parameters [6][7][8][9][10][11][12]. The key point of research has been to develop an efficient system that leads to a higher material removal rate.…”