EDM of Al7075-B4C-flyash hybrid metal matrix nano-composites and optimization of sustainable measures using genetic algorithm

Abstract

Sustainability is an important approach in today’s manufacturing environment to achieve overall efficiency in terms of economic, environmental and social aspects. This research work aims to investigate the applicability of sustainability in electrical discharge machining (EDM) of Al7075-B4C-flyash hybrid metal matrix nano-composites (HMMNCs). The machining experiments are conducted using central composite design with voltage (V), current (I), pulse-on-time (Ton) and pulse-off-time (Toff) as process parameters and surface roughness and power consumption are being sustainable measures. Mathematical predictive models were developed using response surface methodology (RSM). The predicted performance of the model shows an error percentage 3.76% and 3.97% for surface roughness and power consumption respectively. The experimental results obtained are analysed using 3D contour plots and current and pulse-on-time found most dominating parameters. The sustainable measures are optimized simultaneously using the popular optimization tool i.e., genetic algorithm. The Pareto optimal fronts provide different optimum cutting conditions for production of components with minimum power consumption satisfying the desired surface roughness value. The approach is found to be an effective tool and can be developed with minimum effort and help shop floor engineer towards sustainable machining approach.