A particle swarm approach for grinding process optimization analysis

Optimization is necessary for the control of any process to achieve better product quality, high productivity with low cost. The grinding of silicon carbide is not an easy task due to its low fracture toughness, therefore making the material sensitive to cracking. The efficient grinding involves the optimal selection of operating parameters to maximize the material removal rate (MRR) while maintaining the required surface finish and limiting surface damage. In this work, optimization based on the available model has been carried out to obtain optimum parameters for silicon carbide grinding via particle swarm optimization (PSO) based on the objective of maximizing MRR with reference to surface finish and damage. Based on statistical analysis for various constraint values of surface roughness and number of flaws, simulation results obtained for this machining process for PSO are comparatively better to genetic algorithm (GA) approach. In addition, the post-optimal robustness of PSO has also been studied. From simulation results together with the proposed robustness measurement method, it has been shown that PSO is a convergent stable algorithm.