Real-time Monitoring of Tool States in Drilling Process Combined with GA-BP and Ensemble Learning
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摘要: 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。
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关键词:
- 钻削过程 /
- 状态监测 /
- LabVIEW /
- 集成学习 /
- GA-BP-Adaboost
Abstract: 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.-
Key words:
- drilling process /
- condition monitoring /
- LabVIEW /
- ensemble learning /
- GA-BP-Adaboost
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表 1 钻头的主要磨损形式
Table 1. Wear form of a drill bit
磨损形式 具体类型 产生原因 前刀面磨损 切削温度过高, 运动中的钻头将与其粘接的工件材料带走, 从而产生“月牙洼” 正常磨损 后刀面磨损 后刀面与工件持续挤压与摩擦, 产生较大的压力, 引起后刀面磨损 横刃磨损 横刃主要承担定心的作用, 受到的轴向力最大, 且回转半径较小, 切削时挤压严重, 容易造成磨损 崩刃 切削刃上应力较集中且受机械振动及切屑的影响, 容易产生崩刃 非正常磨损 剥落 切削不稳定或钻头承受交变接触应力, 易产生表层剥落 折断 钻头材料有缺陷, 在恶劣条件下加工, 可能造成钻头折断 表 2 预实验切削参数
Table 2. Pre experimental cutting parameters
编号 主轴转速/(r·min-1) 进给量/(mm·min-1) 钻孔深度/mm 机床负载/% 1 270 15 12 30~40 2 270 10 12 20~30 3 270 15 18 30~50 4 300 10 12 25~35 5 300 15 12 30~60 表 3 标签划分标准
Table 3. Label classification standards
刀具磨损值VB 刀具磨损状态 标签 VB≤0.2 mm 初期磨损 [1, 0, 0] 0.2 mm≤VB≤0.3 mm 正常磨损 [0, 1, 0] VB≥0.3 mm 急剧磨损 [0, 0, 1] 表 4 弱分类器累计误差和分类权重结果
Table 4. Cumulative error and classification weight results of the weak classifier
序号 累计误差en 分类权重an 1 0.350 0 0.309 5 2 0.304 4 0.413 3 3 0.307 9 0.405 1 4 0.321 8 0.372 8 5 0.332 4 0.348 6 6 0.312 0 0.395 4 7 0.308 7 0.403 2 8 0.309 1 0.402 2 9 0.302 1 0.418 7 10 0.344 4 0.322 0 表 5 模型测试结果
Table 5. Model test results
加工孔数 磨损状态 期望输出 实际输出 2 初期磨损 [1, 0, 0] [0.872 3, 0.008 9, 0.045 3] 7 初期磨损 [1, 0, 0] [0.694 5, 0.397 7, 0.001 5] 15 正常磨损 [0, 1, 0] [0.006 2, 0.816 0, 0.117 8] 24 正常磨损 [0, 1, 0] [-0.115 7, 1.215 9, 0.390 1] 35 急剧磨损 [0, 0, 1] [0.005 4, 0.334 2, 0.641 9] 38 急剧磨损 [0, 0, 1] [0.140 5, -0.064 2, 1.168 0] -
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