Chatter identification of thin-walled parts for intelligent manufacturing based on multi-signal processing
作者机构：School of Mechanical EngineeringTongji UniversityShanghai 201804People's Republic of China Bosch Rexroth Endowed Chair for Automation&Electrification SolutionsSino-German College for Postgraduate StudyTongji UniversityShanghai 201804People's Republic of China China North Engine Research InstituteTianjin 300400People's Republic of China WBK Institute of Production ScienceKarlsruhe Institute of TechnologyKarlsruhe 76131Germany
出 版 物：《Advances in Manufacturing》 (先进制造进展（英文版）)
年 卷 期：2021年第9卷第1期
页 面：22-33页
核心收录：
学科分类：02[经济学] 0202[经济学-应用经济学] 0817[工学-化学工程与技术] 0807[工学-动力工程及工程热物理] 0805[工学-材料科学与工程（可授工学、理学学位）] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0801[工学-力学（可授工学、理学学位）]
基 金：support from the National Key R&D Program of China(Grant No.2017YFEO101400) also appreciate reviewers for their critical comments.
主 题：Chatter Milling force Acceleration Wavelet packet decomposition Multi-sensor
摘 要：Machine chatter is still an unresolved and challenging issue in the milling process,and developing an online chatter identification and process monitoring system towards smart manufacturing is an urgent requirement.In this paper,two indicators of chatter detection are investigated.One is the real-time variance of milling force signals in the time domain,and the other one is the wavelet energy ratio of acceleration signals based on wavelet packet decomposition in the frequency domain.Then,a novel classification concept for vibration condition,called slight chatter,is proposed and integrated successfully into the designed multi-classification support vector machine(SVM)model.Finally,a mapping model between image and chatter indicators is established via a distance threshold on the image.The multi-SVM model is trained by the results of three signals as an input.Experiment data and detection accuracy of the SVM model are verified in actual machining.The identification accuracy of 96.66%has proved that the proposed solution is feasible and effective.The presented method can be used to select optimized milling parameters to improve machining process stability and strengthen manufacturing system monitoring.
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