LAUSR

This article studied the relationship between surface roughness and surface micro hardness of the hard-brittle materials and the process parameters in quick-point grinding, and then established the prediction model for the surface micro hardness and surface roughness by the back propagation network which was an improved genetic algorithm. Through the experiments of the quick-point grinding of ceramics, surface roughness and surface micro hardness were tested, and reliability of the model was validated thereby. Based on the least square fitting of the experiment value and prediction value, the one-dimensional analytic model for surface roughness and surface micro hardness had been, respectively, developed in terms of grinding speed, grinder work-table feed speed, grinding depth, incline angle, and deflection angle as process parameters. Both the correlation test and experiment verification indicated that the model exhibited a high level of accuracy. The multivariate model of surface roughness and surface micro hardness can be constructed by means of immune algorithms and orthogonal experiment data. With the optimum objective of the minimum surface roughness and maximum surface micro hardness, a set of optimized process parameters was obtained using immune algorithms, and experiment verification proved that the error value was less than 10%.