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基于AdaBoost算法实现的机床刀具磨损节能集成研究
马瑞 2024/12/15 18:48:09
淮安生物工程高等职业学校，机电工程系，江苏淮安 223002
摘要：为了提高刀具磨损状态识别能力，开发出了刀具磨损阶段建立模型方法。把AdaBoost集成算法加入模型，降低磨损过程中模型预测误差。研究结果表明：平稳磨损阶段所需时间最短，最长为急剧磨损阶段。进行刀具磨损识别期间，集成学习算法可以获得比单独算法更优性能。磨损期间误差受到各阶段磨损变化率的较大影响。采用集成方法AdaBoost得到了较小MAE，只有36.3%，可以有效促进非集成算法模型的性能提升，实现集成学习算法模型的改善效果。
关键词：刀具磨损；模型；集成算法；状态识别
中图分类号：TG156
Research on tool wear energy-conservation integration of machine tools based on AdaBoost algorithm
Ma Rui
Department of Mechanical and Electrical Engineering, Huaian Higher Vocational School of Biological Engineering, Huaian 223002, China
Abstract: In order to improve the ability of tool wear state recognition, a regression model of tool wear stage was developed. AdaBoost integrated algorithm is added to the regression model to reduce the prediction error of the regression model in the process of wear. The results show that the time required for smooth wear stage is the shortest, while the time required for sharp wear stage is the longest. During tool wear identification, the integrated learning algorithm can obtain better performance than the single algorithm. The error during wear is greatly affected by the rate of wear change at each stage. The integration method AdaBoost obtained a small MAE, only 36.3%, which can effectively promote the performance improvement of the non-integrated algorithm model and achieve the improvement effect of the integrated learning algorithm model.
Key words: Tool wear; Regression model; Integration algorithm; State recognition
刀具是数控机床上的关键设备，去受刀具磨损因素影响而引起机床运行停止的情况时有发生，已经成为机床故障的主要方式之一[1-2]。可以通过一些检测手段对刀具磨损状态进行检测，进而采用智能控制技术进行磨损状态识别。那么如果整理并获取刀具磨损状态数据是一个热点方向[3]。
目前，有许多学者针对刀具磨损机制开展研究，并取得了一定的研究成果。例如，杨莉等[4]提出基于堆叠稀疏自动编码器和多传感器特征融合的刀具磨损预测方法，应用磨损数据集来验证预测性能,证明学习方法可以提高预测性能。高鸣等[5]通过采集主轴振动信号来实现铣刀磨损状态监测，使用小波阈值降噪消除信号干扰，引入双向长短周期记忆网络对特征信号编码，通过全连接层预测刀具磨损状态。周粤等[6]提出将希尔伯特黄变换与卷积神经网络相结合的监测方法，运用卷积神经网络生成刀具磨损监测迁移模型，改进后希尔伯特黄变换能避免虚假固有模态分量问题，监测效果得到提升。
本文选择铣削加工刀具作为测试对象，按照具体阶段构建刀具磨损模型后，把（未完，下一页）
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