基于特征迁移的面铣刀磨损监测方法  

Feature Transfer-Based Approach for Tool Wear Monitoring of Face Milling

作　　者：周慧慧 张执南[1,2] ZHOU Huihui;ZHANG Zhinan(School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;State Key Laboratory of Mechanical System and Vibration,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]上海交通大学机械与动力工程学院,上海200240 [2]上海交通大学机械系统与振动国家重点实验室,上海200240

出　　处：《摩擦学学报》2022年第6期1267-1277,共11页Tribology

基　　金：机械系统与振动国家重点实验室项目(MSVZD201912,MSVZD202108)资助。

摘　　要：实时监测刀具磨损状态对保证工件加工质量和确定合理换刀时间至关重要.数据驱动的多源信号融合预测是解决刀具磨损预测难题的可行方案.本文中通过时域和频域分析提取了多维信号特征,并结合机器视觉方法处理刀具磨损图像获得的磨损特征,针对涂层面铣刀建立了随机森林磨损预测模型.对于同类型的刀具和工件材料,使用特征迁移方法解决多工况场景下新刀样本不足问题.试验结果表明,基于迁移特征建立的磨损预测模型对目标刀具的磨损量预测效果较迁移前显著提升,准确性评价指标R^(2)决定系数从0.37提升到0.96.基于特征迁移的磨损预测模型为数据驱动模型在刀具磨损预测和实时监测领域的应用提供参考依据.The tool wear plays a crucial role in determining the quality of the workpiece.Excessive tool wear results in a decrease in machining accuracy and speed and a decline in yield.At the same time,frequent tool changes increases costs and affects the processing speed as well.Therefore,it becomes essential to accurately determine the tool wear status to plan a reasonable tool change time and even further optimize the tool design.Data-driven wear prediction based on multi-sensor signal processing proves to be a feasible solution to this problem.However,the model trained in one working condition is usually not able to fit another condition well.Sometimes the tool wear characteristic may be a different even for the same kind of tool under the same working condition,limiting the practical application of this method in the industry.Furthermore,the acquisition of the tool wear ground truth generally relies on offline detection with the microscope,which leads to low efficiency of the data labeling.In response to the above problems,we conducted experiments on the wear prediction problem of face milling cutters based on the framework of tribological informatics.In this study,force/torque sensors,vibration sensors,and acoustic emission sensors were selected as the input sources for the training and predictions of tool wear models,recording the signals generated during the processing of the workpiece.Specifically designed filters firstly filtered the original signal to reduce the interference of non-cutting factors.A series of statistics from time and frequency domain analyses,e.g.the mean,variance,and extreme values,were then extracted as the signal features.To obtain the ground truth of the tool wear,a CMOS camera was installed on the machining platform capturing pictures of the wear position of the cutter.We proposed a machine vision-based tool wear recognition method.With the prior knowledge of the wear morphology of the face milling cutter,the method uses image preprocessing,Canny edge extraction,and region recognition to a

关 键 词：刀具磨损 数据驱动 迁移学习 磨损预测 摩擦信息学 

分 类 号：TH117.1[机械工程—机械设计及理论]