Improved differential evolution approach for optimization of surface grinding process

An improved differential evolution algorithm, named the Taguchi-sliding-based differential evolution algorithm (TSBDEA), is proposed in this work to solve the problem of optimization for the surface grinding process. The purpose of this work is to optimize the grinding variables such as wheel speed, workpiece speed, depth of dressing, and lead of dressing, using a multi-objective function model with a weighted approach, simultaneously subject to a comprehensive set of process constraints. The TSBDEA, a powerful global numerical optimization method, combines the differential evolution algorithm (DEA) with the Taguchi-sliding-level-method (TSLM). The TSLM is used as the crossover operation of the DEA. Then, the systematic reasoning ability of the TSLM is provided to select the better offspring to achieve the crossover, and consequently enhance the DEA. Therefore, the TSBDEA can be statistically sound and quickly convergent. The illustrative cases of both rough-grinding and finish-grinding are given to demonstrate the applicability of the proposed TSBDEA, and the computational results show that the proposed TSBDEA can obtain better results than the methods presented in the literatures.