MOPSO process parameter optimization in ultrasonic vibration-assisted grinding of hardened steel

Abstract

Ultrasonic vibration-assisted grinding (UVAG) is frequently prescribed as an effective technique to improve the grindability of di cult-to-cut materials, earning tremendous application opportunities in the industrial eld. However, the traditional optimization of grinding parameters requires substantial experimental analyses and is prone to fall into a local optimum. In this study, a multiobjective particle swarm optimization (MOPSO) model for grinding forces and surface roughness is established on the basis of comparative experiments between UVAG and conventional grinding. Optimized process parameters are then used to conduct ultrasonic vibration-assisted pro le grinding experiments. Results show that the tangential and normal grinding forces are reduced by 20.51% and 18.91%, respectively, and the ground surface roughness (R a ) is decreased by 9.47%. In addition, the sharpness of grinding wheels can be maintained for UVAG. A Pareto solution set with 15 noninferior solutions is obtained using the MOPSO algorithm, suggesting that the good surface roughness is realized using larger wheel speed and cutting depth and a smaller feed speed. Finally, forming workpieces with excellent shape accuracy and high surface quality, as well as optimized machining parameters, are achieved under the ultrasonic vibration-assisted pro le grinding process.