Chatter feature extraction for milling thin-walled parts based on GWO-VMD and CMSE

Chatter feature extraction for milling thin-walled parts based on GWO-VMD and CMSE | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Chatter feature extraction for milling thin-walled parts based on GWO-VMD and CMSE Xuezhi Wang, Chongli You, Xiaoguang Li, Shujuan Ma, Ning Hou, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5315011/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 May, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract To meet the requirements of the aviation industry for lightweight and high-strength equipment, a large number of complex monolithic thin-walled parts are used in aircraft. Most of these parts are processed by CNC milling, and chatter is easily induced during the machining process, which affects the machining quality of thin-walled parts. Therefore, how to apply signal processing technology to accurately identify chatter under complex working conditions is extremely important. To address this problem, this paper proposes a milling chattering feature extraction method based on the VMD of the grey wolf optimizer (GWO) and the composite multiscale entropy (CMSE). First, the milling force signal was adaptively decomposed into multiple intrinsic mode functions (IMFs) using GWO-VMD with the minimum permutation entropy (PE) as the fitness function of the GWO. Next, the signal was reconstructed according to the energy ratio of the decomposed signal to improve the signal-to-noise ratio. Then, the CMSE of the reconstructed signal was extracted and analyzed, and the CMSE of the optimal scale was selected as the chatter detection index to achieve a more accurate chatter detection. Finally, experimental verification was carried out by side milling of thin-walled parts. The results show that the processing of the signal using the optimized variational modal decomposition algorithm can avoid the problem of difficult separation of the chattering signals due to mode mixing. Compared with the empirical mode decomposition (EMD), the variation rate of the CMSE obtained after processing the signal with the GWO-VMD method is increased by 51.38%, which can significantly improve the discrimination ability of the CMSE for different milling states and is more conducive to chatter detection. Among them, the CMSE with a scale factor of 3 is most beneficial to chatter detection. The realization of chatter detection of thin-walled parts can effectively control the chatter generated in the milling process of thin-walled parts, which is conducive to improving the machining accuracy, quality and efficiency of workpieces. It is significant for the development of the intelligent manufacturing industry and the aerospace industry. Feature extraction Milling chatter Variational mode decomposition Gray wolf optimizer Composite multiscale entropy Full Text Cite Share Download PDF Status: Published Journal Publication published 14 May, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Minor Revisions Needed 26 Apr, 2025 Reviewers agreed at journal 26 Oct, 2024 Reviewers invited by journal 26 Oct, 2024 Editor assigned by journal 24 Oct, 2024 First submitted to journal 22 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5315011","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":370681905,"identity":"b5c06353-1a9f-46ff-a3dc-8280e9e3259a","order_by":0,"name":"Xuezhi 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Most of these parts are processed by CNC milling, and chatter is easily induced during the machining process, which affects the machining quality of thin-walled parts. Therefore, how to apply signal processing technology to accurately identify chatter under complex working conditions is extremely important. To address this problem, this paper proposes a milling chattering feature extraction method based on the VMD of the grey wolf optimizer (GWO) and the composite multiscale entropy (CMSE). First, the milling force signal was adaptively decomposed into multiple intrinsic mode functions (IMFs) using GWO-VMD with the minimum permutation entropy (PE) as the fitness function of the GWO. Next, the signal was reconstructed according to the energy ratio of the decomposed signal to improve the signal-to-noise ratio. Then, the CMSE of the reconstructed signal was extracted and analyzed, and the CMSE of the optimal scale was selected as the chatter detection index to achieve a more accurate chatter detection. Finally, experimental verification was carried out by side milling of thin-walled parts. The results show that the processing of the signal using the optimized variational modal decomposition algorithm can avoid the problem of difficult separation of the chattering signals due to mode mixing. Compared with the empirical mode decomposition (EMD), the variation rate of the CMSE obtained after processing the signal with the GWO-VMD method is increased by 51.38%, which can significantly improve the discrimination ability of the CMSE for different milling states and is more conducive to chatter detection. Among them, the CMSE with a scale factor of 3 is most beneficial to chatter detection. The realization of chatter detection of thin-walled parts can effectively control the chatter generated in the milling process of thin-walled parts, which is conducive to improving the machining accuracy, quality and efficiency of workpieces. 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