Yuan Jun, Liu Libing, Yang Zeqing, Zhang Yanrui
School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China.
Experimental Training, Hebei University of Technology, Tianjin 300401, China.
Sensors (Basel). 2020 Oct 27;20(21):6113. doi: 10.3390/s20216113.
Most online tool condition monitoring (TCM) methods easily cause machining interference. To solve this problem, we propose a method based on the analysis of the spindle motor current signal of a machine tool. Firstly, cutting experiments under multi-conditions were carried out at a Fanuc vertical machining center, using the Fanuc Servo Guide software to obtain the spindle motor current data of the built-in current sensor of the machine tool, which can not only apply to the actual processing conditions but, also, save costs. Secondly, we propose the variational mode decomposition (VMD) algorithm for feature extraction, which can describe the tool conditions under different cutting conditions due to its excellent performance in processing the nonstationary current signal. In contrast with the popular wavelet packet decomposition (WPD) method, the VMD method was verified as a more effective signal-processing technique according to the experimental results. Thirdly, the most indicative features that relate to the tool condition were fed into the ensemble learning (EL) classifier to establish a nonlinear mapping relationship between the features and the tool wear level. Compared with existing TCM methods based on current sensor signals, the operation process and experimental results show that using the proposed method for the monitoring signal acquisition is suitable for the actual processing conditions, and the established tool wear prediction model has better performance in both accuracy and robustness due to its good generalization capability.
大多数在线刀具状态监测（TCM）方法容易导致加工干扰。为了解决这个问题，我们提出了一种基于机床主轴电机电流信号分析的方法。首先，在发那科立式加工中心进行了多工况切削实验，利用发那科伺服向导软件获取机床内置电流传感器的主轴电机电流数据，该数据不仅适用于实际加工工况，还能节省成本。其次，我们提出了用于特征提取的变分模态分解（VMD）算法，由于其在处理非平稳电流信号方面的优异性能，该算法能够描述不同切削条件下的刀具状态。与流行的小波包分解（WPD）方法相比，根据实验结果验证了VMD方法是一种更有效的信号处理技术。第三，将与刀具状态最相关的特征输入到集成学习（EL）分类器中，以建立特征与刀具磨损程度之间的非线性映射关系。与现有的基于电流传感器信号的TCM方法相比，操作过程和实验结果表明，使用所提出的方法进行监测信号采集适用于实际加工工况，并且由于其良好的泛化能力，所建立的刀具磨损预测模型在准确性和鲁棒性方面都具有更好的性能。
