结合与集成学习的钻削过程刀具状态实时监测

Real-time Monitoring of Tool States in Drilling Process Combined with GA-BP and Ensemble Learning

为了能够有效识别钻削过程中刀具的磨损状态,为工厂实际加工过程提供刀具磨损的及时预警,开发了一种基于LabVIEW的钻削刀具磨损状态监测平台。平台可以实现实时采集振动信号并进行时域、频域和时频域的特征提取和数据保存。通过将遗传优化算法、BP神经网络与集成学习结合,构建了GA-BP-Adaboost模型,借助LabVIEW与MATLAB混合编程实现了模型搭建。最后,经过钻削实验分析实时信号及其多种特征对钻头刀具磨损状态的的表征情况,选择三层小波包分解的1、6、8频带作为模型的输入数据训练模型,经测试,模型的分类精度在90%以上。同时,平台的实时响应时间不超过3 s,可以满足实际加工过程的要求。

In order to effectively identify the tool wear states in the drilling process and provide timely warning of tool wear for the actual machining process in the factory,a condition monitoring platform for tool wear state of drilling based on LabVIEW is developed.The platform can realize real-time vibration signal acquisition,feature extraction and data preservation in time domain,frequency domain and time-frequency domain.By combining genetic optimization algorithm(GA),Back propagation(BP)neural network and ensemble learning,the GA-BP-Adaboost model is constructed,and the model is built by LabVIEW and MATLAB mixed programming.Finally,through actual drilling experiments,the characterization of real-time signals and their various characteristics on the wear states of drill tools is analyzed,the 1,6 and 8 frequency bands of three-layer wavelet packet decomposition are selected as the input data of the model to train the GA-BP-Adaboost model,the classification accuracy of the model is above 90%.At the same time,the real-time response time of the platform is not more than 3 seconds,which can meet the requirements of the actual machining process.