Nonlinear Chen-Lee Chaotic System Based Deep Convolutional Generative Adversarial Nets for Chatter Diagnosis

Nonlinear Chen-Lee Chaotic System Based Deep Convolutional Generative Adversarial Nets for Chatter Diagnosis | 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 Nonlinear Chen-Lee Chaotic System Based Deep Convolutional Generative Adversarial Nets for Chatter Diagnosis Ping-Huan Kuo, Po-Chien Luan, Yung-Ruen Tseng, Her-Terng Yau This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1066378/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Chatter has a direct effect on the precision and life of machine tools and its detection is a crucial issue in all metal machining processes. Traditional methods focus on how to extract discriminative features to help identify chatter. Nowadays, deep learning models have shown an extraordinary ability to extract data features which are their necessary fuel. In this study deep learning models have been substituted for more traditional methods. Chatter data are rare and valuable because the collecting process is extremely difficult. To solve this practical problem an innovative training strategy has been proposed that is combined with a modified convolutional neural network and deep convolutional generative adversarial nets. This improves chatter detection and classification. Convolutional neural networks can be effective chatter classifiers, and adversarial networks can act as generators that produce more data. The convolutional neural networks were trained using original data as well as by forged data produced by the generator. Original training data were collected and preprocessed by the Chen-Lee chaotic system. The adversarial training process used these data to create the generator and the generator could produce enough data to compensate for the lack of training data. The experimental results were compared with without a data generator and data augmentation. The proposed method had an accuracy of 95.3% on leave-one-out cross-validation over ten runs and surpassed other methods and models. The forged data were also compared with original training data as well as data produced by augmentation. The distribution shows that forged data had similar quality and characteristics to the original data. The proposed training strategy provides a high-quality deep learning chatter detection model. Mechanical Engineering Ocean Engineering generative adversarial network convolutional neural network chatter chaos theory fractional differential masks Full Text Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-1066378","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":68507761,"identity":"811721cc-6c15-4847-aa04-62e2f099b4ec","order_by":0,"name":"Ping-Huan Kuo","email":"","orcid":"","institution":"National Chung Cheng University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ping-Huan","middleName":"","lastName":"Kuo","suffix":""},{"id":68507762,"identity":"d7a9f1a1-bb83-423a-8ffd-61b5f52aea37","order_by":1,"name":"Po-Chien Luan","email":"","orcid":"","institution":"National Chung Cheng University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Po-Chien","middleName":"","lastName":"Luan","suffix":""},{"id":68507763,"identity":"82e887d4-80b9-4b65-87f2-9fcb46de69e9","order_by":2,"name":"Yung-Ruen Tseng","email":"","orcid":"","institution":"National Chung Cheng University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yung-Ruen","middleName":"","lastName":"Tseng","suffix":""},{"id":68507764,"identity":"a1be0e56-4160-433e-b77f-669378dbc3c2","order_by":3,"name":"Her-Terng Yau","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie3RsWrDMBCA4RMGZRF4VSg0r3Am4CSQh5EoOItrCt2LoOApkFUvUegUulXB4C4NXj2mewZn8xBoZWcKNGrGDvo3CT7uhAB8vn+aETi/BQi/dkTxqwQxzUMyBmAB9oR2l8JJgo1uCqmAUU4U/E0mg60pGAaLl9XWcPI2zcKRortDe5nMlpmwhN6v60wg+eSPwxwGkXZMQZOiJcwSQEFyLl9LoDfMRap9R/girgyajrx35OgidYobjShik0aqn0ItcT1/pvdoGhTRuk7uQFqiS5kPl8llMgnTcSOO36O4Kj7aQ/4kV89Fydu5Y7Gz02md/neuJT6fz+f7rR8oPlXrj+3zJQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1187-1771","institution":"National Chung Cheng University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Her-Terng","middleName":"","lastName":"Yau","suffix":""}],"badges":[],"createdAt":"2021-11-10 05:58:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1066378/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1066378/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":16284874,"identity":"3c79ba99-81d4-4b36-9004-38c05cc75dec","added_by":"auto","created_at":"2021-12-08 15:53:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":839010,"visible":true,"origin":"","legend":"","description":"","filename":"GANFOCNNnonlineardynamics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1066378/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"Nonlinear Chen-Lee Chaotic System Based Deep Convolutional Generative Adversarial Nets for Chatter Diagnosis","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1066378/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"generative adversarial network, convolutional neural network, chatter, chaos theory, fractional differential masks","lastPublishedDoi":"10.21203/rs.3.rs-1066378/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1066378/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Chatter has a direct effect on the precision and life of machine tools and its detection is a crucial issue in all metal machining processes. Traditional methods focus on how to extract discriminative features to help identify chatter. Nowadays, deep learning models have shown an extraordinary ability to extract data features which are their necessary fuel. In this study deep learning models have been substituted for more traditional methods. Chatter data are rare and valuable because the collecting process is extremely difficult. To solve this practical problem an innovative training strategy has been proposed that is combined with a modified convolutional neural network and deep convolutional generative adversarial nets. This improves chatter detection and classification. Convolutional neural networks can be effective chatter classifiers, and adversarial networks can act as generators that produce more data. The convolutional neural networks were trained using original data as well as by forged data produced by the generator. Original training data were collected and preprocessed by the Chen-Lee chaotic system. The adversarial training process used these data to create the generator and the generator could produce enough data to compensate for the lack of training data. The experimental results were compared with without a data generator and data augmentation. The proposed method had an accuracy of 95.3% on leave-one-out cross-validation over ten runs and surpassed other methods and models. The forged data were also compared with original training data as well as data produced by augmentation. The distribution shows that forged data had similar quality and characteristics to the original data. 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