摘要
薄壁零件由于其本身的弱刚性,铣削过程中极易发生颤振、变形,从而加剧刀具磨损,为提高薄壁零件的铣削加工效率和表面质量,提出了一种数字孪生与支持向量机(SVM)融合驱动的刀具磨损状态识别方法。利用时、频域分析和小波包变换提取特征向量,通过网格搜索与交叉验证(GSCV)的方法进行超参数寻优,结合SVM算法构建薄壁零件铣削刀具磨损状态识别模型。试验结果表明,SVM算法在高维小样本数据的分类识别问题中优势明显,对于不同铣刀磨损状态的识别准确率分别达到96%和90.16%,具有较好的泛化能力。结合机器学习算法构建高保真、轻量化的数字孪生体,并将其嵌入薄壁零件铣削过程监测平台,以解决加工过程中信号实时监测和刀具磨损状态在线识别的问题。
Due to its weak rigidity,thin-walled parts are prone to chatter and deformation in the milling process,which aggravates tool wear.In order to improve the milling efficiency and surface quality of thin-walled parts,a tool wear state recognition method driven by the fusion of digital twin and support vector machine(SVM) is proposed.The feature vectors are extracted by time-frequency domain analysis and wavelet packet transform.The super parameters are optimized by grid search and cross validation(GSCV).Combined with SVM algorithm,the wear state recognition model of milling tool for thin-walled parts is constructed.The experimental results show that SVM algorithm has obvious advantages in the classification and recognition of high-dimensional and small sample data.The recognition accuracy of different milling cutter wear states reaches 96% and 90.16% respectively,and has good generalization ability.Combined with machine learning algorithm,a high fidelity and lightweight digital twin is constructed and embedded into the milling process monitoring platform of thin-walled parts,so as to solve the problems of real-time signal monitoring and online recognition of tool wear state in the machining process.
作者
宋清华
彭业振
王润琼
刘战强
SONG Qinghua;PENG Yezhen;WANG Runqiong;LIU Zhanqiang(Shandong University,Jinan 250061,China;Key Laboratory of High Efficiency and Clean Mechanical Manufacture,Shandong University,Ministry of Education,Jinan 250061,China)
出处
《航空制造技术》
CSCD
北大核心
2023年第3期46-52,60,共8页
Aeronautical Manufacturing Technology
基金
国家自然科学基金(52275445)
山东省重大科技创新工程(2020CXGC010204)。
关键词
数字孪生
支持向量机
刀具磨损
小波包变换
在线识别
薄壁件
Digital twin
Support vector machine
Tool wear
Wavelet packet decomposition
Online identification
Thin-walled parts
作者简介
宋清华,教授,博士,研究方向为高性能加工技术与装备、加工过程智能监控、生物医用器械。
