Surface Roughness Prediction of Ultra-Precision Grinding for Magnesium Aluminum Spinel

Abstract

Magnesium alumina spinel (MgAl2O4) has excellent properties and is widely used in the field of optics and communication. The high quality and high efficiency of the grinding surface are two important issues in the grinding process. In this paper, the prediction model of grinding surface roughness of magnesia alumina spinel is established. According to the equivalent response surface and contour map, the influence of spindle speed, feed rate and grinding depth on surface roughness and the interaction of various influencing factors are analyzed. The particle swarm optimization algorithm is used to optimize ultra-precision grinding parameters to ensure high efficiency and high machining quality. The analysis shows that the prediction error of the model is less than 10%, and the optimal grinding process parameters can obtain a higher material removal rate on the premise of ensuring the machining quality, which provides an important basis for the efficient processing of magnesia alumina spinel materials.