Vibration-Signal-Driven Generative Framework for Automated Nut Thread Quality Inspection in Tapping Processes

Vibration-Signal-Driven Generative Framework for Automated Nut Thread Quality Inspection in Tapping Processes | 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 Vibration-Signal-Driven Generative Framework for Automated Nut Thread Quality Inspection in Tapping Processes Kuei-Jung Hung, Jau-Woei Perng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7835911/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jan, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract Tapping is a widely used threading process in manufacturing, where variations in machining conditions often lead to deviations in nut internal thread quality. Traditional inspection methods rely on either direct imaging or contact gauges, both of which are time-consuming and difficult to integrate into real-time production. To address these challenges, this study proposes two vibration-signal-driven generative frameworks for nut internal thread image synthesis. The first is a Wasserstein Generative Adversarial Network (WGAN)-based approach with a class-aware discriminator that leverages vibration signals as conditional embeddings in the generator, while incorporating nut quality labels in the discriminator to enhance class fidelity. The second is a Conditional Latent Diffusion Model (CLDM)-based framework that employs a Transformer encoder to effectively embed tapping vibration signals into the diffusion process for improved diversity and distributional coverage. A comprehensive evaluation protocol was established, integrating perceptual metrics (MS-SSIM, LPIPS), distributional metrics (Precision, Recall, Density, Coverage), and statistical metrics (FID and a domain-specific Custom FID). Experimental results show that while the CLDM-based method achieves superior fidelity and diversity, the WGAN-based framework offers higher computational efficiency and flexibility in conditional embedding. Importantly, both approaches preserve discriminative features sufficient for downstream nut quality prediction, with tapping-phase vibration signals proving most informative. The proposed frameworks provide practical and non-destructive solutions for automated nut quality inspection. By converting readily available vibration signals into realistic thread images, they reduce inspection time, labor cost, and dependence on direct imaging, thereby enhancing efficiency and scalability in industrial manufacturing. Signal-to-Image Wasserstein Generative Adversarial Network Non-Destructive Testing Latent Diffusion Model Nuts Quality Prediction Full Text Cite Share Download PDF Status: Published Journal Publication published 28 Jan, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 15 Dec, 2025 Reviewers agreed at journal 02 Nov, 2025 Reviewers invited by journal 16 Oct, 2025 Editor assigned by journal 15 Oct, 2025 First submitted to journal 13 Oct, 2025 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-7835911","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530649951,"identity":"01328b50-244f-487e-9ea1-30266efae56c","order_by":0,"name":"Kuei-Jung Hung","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kuei-Jung","middleName":"","lastName":"Hung","suffix":""},{"id":530649953,"identity":"5870ea2a-2718-45ae-96e9-f7c1b662fddc","order_by":1,"name":"Jau-Woei 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Traditional inspection methods rely on either direct imaging or contact gauges, both of which are time-consuming and difficult to integrate into real-time production. To address these challenges, this study proposes two vibration-signal-driven generative frameworks for nut internal thread image synthesis. The first is a Wasserstein Generative Adversarial Network (WGAN)-based approach with a class-aware discriminator that leverages vibration signals as conditional embeddings in the generator, while incorporating nut quality labels in the discriminator to enhance class fidelity. The second is a Conditional Latent Diffusion Model (CLDM)-based framework that employs a Transformer encoder to effectively embed tapping vibration signals into the diffusion process for improved diversity and distributional coverage. A comprehensive evaluation protocol was established, integrating perceptual metrics (MS-SSIM, LPIPS), distributional metrics (Precision, Recall, Density, Coverage), and statistical metrics (FID and a domain-specific Custom FID). Experimental results show that while the CLDM-based method achieves superior fidelity and diversity, the WGAN-based framework offers higher computational efficiency and flexibility in conditional embedding. Importantly, both approaches preserve discriminative features sufficient for downstream nut quality prediction, with tapping-phase vibration signals proving most informative. The proposed frameworks provide practical and non-destructive solutions for automated nut quality inspection. By converting readily available vibration signals into realistic thread images, they reduce inspection time, labor cost, and dependence on direct imaging, thereby enhancing efficiency and scalability in industrial manufacturing.\u003c/p\u003e","manuscriptTitle":"Vibration-Signal-Driven Generative Framework for Automated Nut Thread Quality Inspection in Tapping Processes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 15:44:11","doi":"10.21203/rs.3.rs-7835911/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2025-12-15T08:29:38+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-11-02T23:57:24+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-16T12:46:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-15T22:09:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2025-10-13T10:38:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d51e6829-5dbd-4838-b642-71e07bc2f291","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:09:11+00:00","versionOfRecord":{"articleIdentity":"rs-7835911","link":"https://doi.org/10.1007/s00170-026-17492-0","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2026-01-28 15:59:18","publishedOnDateReadable":"January 28th, 2026"},"versionCreatedAt":"2025-11-03 15:44:11","video":"","vorDoi":"10.1007/s00170-026-17492-0","vorDoiUrl":"https://doi.org/10.1007/s00170-026-17492-0","workflowStages":[]},"version":"v1","identity":"rs-7835911","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7835911","identity":"rs-7835911","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}