Intelligent Modeling and Multiobjective Optimization of Electric Discharge Diamond Grinding

The grinding of metal matrix composites (MMCs) is very difficult by conventional techniques due to its improved mechanical properties. It often results in poor surface quality (surface damage) in the form of surface cracks/residual stresses and requires frequent truing and dressing due to clogging of the grinding wheel. The electric discharge diamond grinding (EDDG), a hybrid process of electric discharge machining and grinding may overcome these problems up to some extent. But low material removal rate (MRR) and high wheel wear rate (WWR) are the main problems in EDDG to achieve economic performance. The present paper investigates the EDDG process performance during grinding of copper-iron-graphite composite by modeling and simultaneous optimization of two important performance characteristics such as MRR and WWR. A hybrid approach of artificial neural network, genetic algorithm, and grey relational analysis has been proposed for multi-objective optimization. The verification results show considerable improvement in the performance of both quality characteristics.