Evaluation of Wear Condition in End Milling Cutter with Random Forest Algorithm
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摘要: 制造业自动化水平的提高对数控机床的自我诊断能力提出了新的要求，而人工智能的发展为此开辟了新的解决方案。为了更高效、全面地对刀具磨损状态进行评估，通过采集立铣刀切削时的力和加速度信号，并对其时域、频域与小波能量特征的信号的特征值进行提取，建立了一种基于随机森林算法（Random forest）的刀具磨损状态评估模型。实验数据的对比验证中，随机森林模型对107组测试样本的刀具磨损状态评估准确率达到99.1%，且其建立模型的时间少于1 s。结果表明，随机森林算法具有高效与高准确度的特点，能为刀具磨损状态的在线监测系统的建立奠定基础。Abstract: The new requirement for the self-diagnosis ability of CNC machine tools has been put forward in order to improve the manufacturing automation level, and the development of artificial intelligence has opened aiming at this purpose. In order to evaluate the tool wear state more efficiently and comprehensively, the force and acceleration signals in the end milling are collected. The eigenvalues of the time domain, frequency domain and wavelet energy of the signals are extracted. And a model for evaluating the tool wear state via Random Forest algorithm is established. In the comparative verification of the experimental data, the accuracy of the tool wear state of the 107 sets of test samples by using the random forest model is of 99.1%, and the time for establishing the model is below 1 s. The result shows that the random forest algorithm has the characteristics of high efficiency and high accuracy, which lays a foundation for establishing the online monitoring system for tool wear state.
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Key words:
- machine learning /
- wear of tool /
- feature extraction /
- random forest algorithm
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表 2 加工参数表
参数名称 数值 主轴转速 10 400 r/min 进给速率 1 555 mm/min Y向铣削宽度 0.125 mm Z向铣削深度 0.2 mm 采样频率 50 kHz 
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表 3 信号采集设备型号
设备类型 设备型号 机床 Roders Tech RFM760 测力仪 Kistler 9265B三向测力仪 振动传感器 Kistler 8636C振动传感器 铣削材料 不锈钢HRC52 刀具 球头硬质合金立铣刀 采集卡 NI DAQ PCI 1200采集卡 磨损测量器 LEICA MZ12显微镜
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表 4 样本的训练集与测试集划分表
磨损状态 样本数 类别 抽取组数 初期磨损
(1~18)18 训练集
测试集12
6正常磨损
(19~126)188 训练集
测试集124
64急剧磨损
(127~315)109 训练集
测试集72
37
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表 5 随机森林模型对测试集样本的状态评估结果
磨损状态 测试集
样本数评估准确
样本数分类准
确率/%训练
时间/s初期磨损 6 6 100 0.991 正常磨损 64 64 100 0.991 急剧磨损 37 36 97.3 0.991 总计 107 106 99.07 0.991
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