基于声发射信号的带材剪切刀具磨损在线监测方法  

Tool wear online monitoring during shearing process strip based on acoustic emission signal

作　　者：李令 阎秋生[1] 李锴 朱超睿 LI Ling;YAN Qiu-sheng;LI Kai;ZHU Chao-rui(School of Electromechanical Engineering,Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]广东工业大学机电工程学院,广东广州510006

出　　处：《机电工程》2023年第7期1102-1111,共10页Journal of Mechanical & Electrical Engineering

基　　金：国家自然科学基金资助项目(51575112)。

摘　　要：在铁基纳米晶合金带材剪切加工过程中,其刀具的状态对于保证加工质量至关重要。针对铁基纳米晶合金带材剪切加工过程中的刀具磨损状态监测问题,提出了一种基于声发射信号的剪切刀具磨损在线监测方法。首先,通过搭建声发射监测设备确定了相应的参数,采集原始声发射信号进行了预处理,得到了剪切加工阶段的信号,将其用于后续处理;然后,分析了剪切刀具磨损以及带材质量随剪切加工过程变化的关系,并根据剪切加工过程中获取的声发射信号,进行了时域、频域、时频域特征提取,分析了获得的特征与刀具磨损之间的关系,利用ReliefF和主成分分析(PCA)算法进行了特征选择与降维处理,得到了具有良好相关性的特征;最后,基于所选特征,构建了支持向量机(SVM)人工智能模型,用以识别剪切刀具的磨损阶段。研究结果表明:随着刀具磨损的加剧,带材质量下降,声发射信号特征值与刀具磨损存在对应关系;采用ReliefF-PCA-SVM模型能够实现95.56%的分类准确率,能够有效地对剪切加工过程中的刀具磨损进行在线监测。The tool condition during the shearing process of iron-based nanocrystalline alloy strips was critical to ensure the processing quality.To address the problem of tool wear monitoring during shear processing of iron-based nanocrystalline alloy strips,a shear tool wear online monitoring method based on acoustic emission signal was proposed.First,the acoustic emission monitoring equipment was built to determine the corresponding parameters,and the raw acoustic emission signal was collected for pre-processing to obtain the signal of shear processing stage for subsequent processing.Then,the relationship between shear tool wear and strip quality changes during the shear processing was studied,and according to the acoustic emission signal obtained during the shear processing,the time domain,frequency domain and time-frequency domain features were extracted,and the relationship between the obtained features and tool wear was analyzed.The features with good correlation were obtained by using ReliefF and principal component analysis(PCA)algorithms for feature selection and dimensionality reduction.Finally,based on the selected features,a support vector machines(SVM)artificial intelligence model was constructed to identify the shear tool wear stages.The results show that:with the increase of tool wear,the strip quality decreases,and there is a correspondence between the acoustic emission signal feature values and tool wear.Using ReliefF-PCA-SVM model can achieve 95.56%classification accuracy,which can effectively monitor tool wear during shear processing online.

关 键 词：声发射监测设备 铁基纳米晶合金 特征选择与降维 主成分分析 支持向量机 RELIEFF算法 

分 类 号：TH117.1[机械工程—机械设计及理论] TG71[金属学及工艺—刀具与模具] TP391.4[自动化与计算机技术—计算机应用技术]