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CNC铣刀磨损状态的大数据分析与预测方法研究 被引量:2

Research on Big Data Analysis and Prediction Method for Wear Status of CNC Milling Cutter
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摘要 为在铣切加工过程中预测铣刀的磨损状态以及时发现并更换将要磨钝的铣刀,以保障产品质量,运用传感器采集CNC铣床在加工过程中铣床及铣刀的振动信号数据,应用大数据方法研究CNC铣刀磨损状态的分析和预测方法。为保证铣刀磨损状态的识别精度、识别稳定性和分析模型的鲁棒性,采用小波包分解理论对铣床x、y、z三向振动信号数据进行降噪处理,提取时域特征和能量特征,筛选出与磨损状态相关性较大的34个特征。应用XGBoost算法建立铣刀磨损状态的数据分析模型,使用宏平均值评估模型性能,结合SMOTE技术对特征向量进行过采样,使各磨损状态类别样本均衡。借助公开的球头铣刀加工数据集对所提方法进行验证,实验结果表明:利用XGBoost算法能正确分析铣刀磨损状态的数据,能识别出铣刀磨损预警阶段。XGBoost算法的预测精度高、稳定性好、泛化能力强,易应用于工业大数据领域。 In order to predict the wear state of the milling cutter during the milling process so as to timely find and replace the milling cutter that would be blunt,and to ensure the quality of products,the sensors was used to collect vibration signal data of CNC milling machine and milling cutter in the processing process,and the big data method was used to study the analysis and prediction method of the wear state of the CNC milling cutter.In order to ensure the recognition accuracy,recognition stability and robustness of the analysis model,the wavelet packet decomposition theory was used to denoise the vibration signal data in x,y and z directions of the milling machine,the time domain features and energy features were extracted,and 34 features were screened out which were closely related to wear state.The XGBoost algorithm was used to establish the data analysis model of the wear state of the milling cutter,and the macro average value was used to evaluate the performance of the model.Combined with SMOTE technique,the feature vector was oversampled to equalize the wear state category samples.The proposed method was verified by the open ball-end milling machining data set.The experimental results show that by using the XGBoost algorithmthe,wear state data of milling cutters can be analyzed correctly and the early warning stage of milling cutter can be identified.XGBoost algorithm has high prediction accuracy,good stability and strong generalization ability,which is easy to be applied in the field of industrial big data.
作者 谢马军 吴永明 XIE Majun;WU Yongming(School of Electromechanical Engineering,Guangdong University of Technology,Guangzhou Guangdong 510006,China)
出处 《机床与液压》 北大核心 2020年第21期105-110,共6页 Machine Tool & Hydraulics
关键词 铣刀 磨损状态 振动 大数据分析 预测方法 XGBoost算法 Milling cutter Wear state Vibration Big data analysis Prediction method XGBoost algorithm
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