Research on tool wear monitoring in drilling process based on APSO-LS-SVM approach
Research on tool wear monitoring in drilling process based on APSO-LS-SVM approach
复制标题
基于APSO-LS-SVM方法的钻井过程刀具磨损监测研究
DOI：
10.1007/s00170-020-05549-7
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发表时间：
2020-06-04
影响因子：
3.4
通讯作者：
He, Ning
中科院分区：
文献类型：
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作者：
Chen, Ni;Hao, Bijun;He, Ning
工具磨损监测被视为智能制造的重要技术，可确保加工质量并提高加工效率。在本文中，提出了基于自适应粒子群优化（APSO）算法和最小二乘支持向量机（LS-SVM）算法的预测模型，以识别钻孔。切割力信号和振动信号用于工具磨损监测。这些信号是通过小波阈值去命中算法进行预处理的。进行多种信号特征提取方法以处理与钻磨损状态有关的样本数据。工具磨损识别模型的平均绝对误差为0.91％，优于在相同条件下的标准LS-SVM算法。
Tool wear monitoring is deemed as an essential technology of the intelligent manufacturing to guarantee the processing quality and improve the machining efficiency. In this paper, a prediction model based on adaptive particle swarm optimization (APSO) algorithm and least squares support vector machine (LS-SVM) algorithm is proposed for the recognition of drill wear. Cutting force signal and vibration signal are used for tool wear monitoring. And these signals are preprocessed through wavelet threshold de-noising algorithm. Multiple signal feature extraction methods are carried out to process the sample data related to drill wear status. The mean absolute error of the tool wear recognition model is 0.91%, better than the standard LS-SVM algorithm under the same condition.