Abrasive Tool Wear Prediction Based on an Improved Hybrid Difference Grey Wolf Algorithm for Optimizing SVM
Abrasive Tool Wear Prediction Based on an Improved Hybrid Difference Grey Wolf Algorithm for Optimizing SVM
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DOI：
10.1016/j.measurement.2021.110247
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发表时间：
2021-11
期刊：
影响因子：
5.6
通讯作者：
Yu-Teng Liang;S. Hu;Wensen Guo;Hong-qun Tang
中科院分区：
文献类型：
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作者：
Yu-Teng Liang;S. Hu;Wensen Guo;Hong-qun Tang
在智能制造时代，有必要对磨料刀具的磨损状况进行实时监测，以防止因刀具破损和磨损而导致的工件质量恶化。以烧结金刚石磨头磨削氧化锆陶瓷孔为例，提出了一种基于改进的支持向量机混合差分灰狼优化算法的磨具磨损预测模型。通过时、频域和小波分析，提取了力和振动信号的特征。磨损实验结果表明，IHDGWO-支持向量机模型的预测准确率为92%，显著高于支持向量机、GWO-支持向量机和DE-支持向量机的68%、80%和72%的预测精度。新的IHDGWO-支持向量机模型为NMBM磨削过程中磨具磨损的在线监测提供了理论和实践方法。
In the era of intelligent manufacturing, it is necessary to monitor the wear condition of abrasive tools in real time to prevent the deterioration of workpiece quality due to tool breakage and wear. A wear prediction model of abrasive tools based on an improved hybrid differential grey wolf optimization algorithm for optimizing support vector machine (IHDGWO-SVM) is proposed based on the grinding of zirconia ceramic holes by sintered diamond grind bits. The features of the force and vibration signals were extracted by time domain, frequency domain and wavelet analysis. The wear experimental results showed that the prediction accuracy of the IHDGWO-SVM model was 92%, which was significantly higher than the prediction accuracy of 68%, 80% and 72% of SVM, GWO-SVM and DE-SVM. The new IHDGWO-SVM model provides a theoretical and practical method for the on-line wear monitoring of abrasive tools during grinding of NMBM.