关键词: BP神经网络, 参数优化, 点磨削, 微晶玻璃

Abstract:

The trends of experimental data were analyzed, the surface roughness and surface hardness were tested in point-grinding low expansion glass ceramics. The numerical models of surface roughness and hardness were established by the least square fitting. The accuracy of the model was tested by coefficient of determination, and the model predictions were compared with experimental data to validate the accuracy of the model. The results indicated that the model has high accuracy. Based on BP neural networks and GA, the multivariate numerical models were built on surface roughness and hardness according to the results of orthogonal experiments. And both of the models were selected as the objective function. Optimization goal was the minimum of surface roughness and the maximum surface hardness, dual objectives optimization was carried out based on GA. A range of the optimal solution was obtained about point grinding process parameters. Experimental validation results indicate that optimal results are reasonable.

Key words: BP neural network, parameter optimization, point grinding;low expansion glass