关键词: 硬质合金, 表面质量预测, 自适应模糊推理系统, 混合田口遗传算法

Abstract:

For improving the surface integrity and processing quality in the precision cylindrical grinding of cemented carbides, and investigating the prediction technique of the surface quality, a prediction model of the surface roughness in the precision cylindrical grinding of YG3 cemented carbide based on the adaptive network-based fuzzy inference system (ANFIS) was proposed. Then, it was improved by the hybrid Taguchi genetic algorithm (HTGA). At last, the grinding parameters and corresponding surface roughness in the experiments was used as the training and testing samples. Trough the comparative analysis of the prediction results in the BP neural network model, traditional ANFIS model and improved ANFIS model, the effectiveness and prediction accuracy of three models were verified. The results show that the prediction accuracy of the improved ANFIS model was better than that of an other two in the distribution of the relative error Er and the size of mean square error EMSRE, and it is an effective prediction method of the surface quality.

Key words: cemented carbide, surface quality prediction, adaptive network-based fuzzy inference system (ANFIS), hybrid Taguchi genetic algorithm (HTGA)