Parametric optimization of micro-tool fabrication through sheet-EDG using nature-inspired algorithms

Sheet electrical discharge grinding (sheet-EDG) is a recently developed novel method for producing high aspect ratio micro-tools. However, due to the presence of varying nature of initial tool eccentricity for fabricating each micro-tool, determining the optimal process parameters for the mentioned process is a difficult task. To overcome the above issue, an alternative experimental investigation called the slit-cut process was conducted, in which tool rotation was not used. Face-centered central composite design-based response surface methodology (RSM) was conducted to produce 20 slit-cuts on sheet electrodes with varying input parameters like voltage, sheet-EDG parameter, and radial infeed. Later, these slit-cuts were assessed and responses like volume removal rate (VRR) and electrode wear rate (EWR) were evaluated. An analysis of variance (ANOVA) is conducted for both VRR and EWR and statistically validated regression equations were developed. Also, the effects of various input parameters on responses are discussed based on the main effect plots and surface plots. Both VRR and EWR were simultaneously optimized using desirability function approach and a variety of nature-inspired optimization algorithms, including the multi-objective bonobo optimizer (MOBO), multi-objective particle swarm optimization (MOPSO), multi-objective grey wolf optimizer (MOGWO), and non-dominated sorting genetic algorithm (NSGA II). Based on the quality of the Pareto front, MOBO performed better than the other techniques. MOBO suggested changing the design ranges of voltage and radial feed from (140–180 V) to (140–156.8 V) and from (2–6 μm/pass) to (5.2–6 μm/pass), respectively.