Multi-objective optimization for surface grinding process using a hybrid particle swarm optimization algorithm

Optimization for the surface grinding process is a problem with high complexity and nonlinearity. Hence, evolutionary algorithms are needed to apply to get the optimum solution of the problem instead of the traditional optimization algorithms. In this work, a hybrid particle swarm optimization (HPSO) algorithm which combines the dynamic neighborhood particle swarm optimization (DN-PSO) algorithm with the strategy of mutation considering constraints is presented to handle multi-objective optimization for surface grinding process. Such four process parameters as wheel speed, workpiece speed, depth of dressing, and lead of dressing are considered as the variables for optimization, and the following three objectives such as production cost, production rate, and surface roughness are used in a multi-objective function model with a weighted approach. Meanwhile, the constraints of thermal damage, wheel wear, and machine tool stiffness are considered. Computational experiments are conducted on cases of both rough grinding and finish grinding, and comparison results with the previously published results obtained by using other optimization techniques shows the efficiency of the proposed algorithm.