AdaBoost Algorithm Enabled Integrated Algorithm of Staged Recognition of Cutting Tool Wear
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摘要: 提出了一种基于AdaBoost(Adaptive boosting)集成算法的刀具磨损全阶段回归模型建模方法。首先，利用获取到的加工过程信号和刀具磨损值，建立刀具磨损拟合曲线，以实现对初期磨损、平稳磨损和急剧磨损3个阶段的准确划分；其次，对加工过程信号进行特征提取，并与相应的刀具磨损值形成3个阶段的数据样本，利用支持向量机分别建立3个磨损阶段的回归模型；再次，利用AdaBoost在全阶段上确定3个磨损阶段回归模型的权重，最终建立刀具磨损状态识别的回归模型；最后，以某铣刀切削过程采集的刀具磨损数据集验证所提出的模型和方法的有效性。Abstract: A regression method of full-stage wear of cutting tool based on the AdaBoost (Adaptive Boosting) integrated algorithm is proposed. Firstly, using the acquired machining process signals and tool wear values, a fitting curve of cutting tool wear is established to achieve an accurate division in the initial wear stage, smooth wear stage, and sharp wear stage. Secondly, the three-stage data samples with the corresponding wear values of cutting tool by extracting the data feature from the machining process signals are obtained. The regression models for the three stages are established with the support vector machine. Thirdly, the AdaBoost algorithm is used to determine the weights of the three regression models in the three stages, and a regression model is established of full-stage wear regression. Finally, the effectiveness of the present model and method is verified with the wear data of a cutting tool collected in the milling cutter.
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Key words:
- cutting tool wear /
- regression model /
- integrated algorithm
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表 3 磨损阶段划分结果
磨损阶段 加工时间/s 走刀次数 初期磨损 t < 324.338 1~75 平稳磨损 324.338≤t < 552.682 76~127 急剧磨损 t>552.682 128~315 
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表 5 分阶段刀刃1磨损识别结果
算法 初期阶段
MAE/μm平稳阶段
MAE/μm急剧阶段
MAE/μm加权综合
MAE/μmAdaBoost 0.985 0.000 0.414 0.480 SVR 1.957 0.004 0.812 0.949 CART 2.886 0.031 0.898 1.219
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