Intelligent assembly and disassembly method for coal mine drill pipe based on object detection

Intelligent assembly and disassembly method for coal mine drill pipe based on object detection | 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 Intelligent assembly and disassembly method for coal mine drill pipe based on object detection Jiangnan Luo, Deyi Zhang, Yixiang Xu, Jianping Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7186415/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The assembly and disassembly of drill pipes are crucial operations in coal mine drilling, and their automation is essential for both worker safety and operational efficiency. However, due to the complex connection mechanisms of drill pipes, the harsh mining environment, and the lack of relevant research, intelligent disassembly remains a significant challenge. This study proposes a novel quadrangular spiral drill pipe and an intelligent assembly and disassembly method. The drill pipe features a convex quadrangular prism male end with a locking pin and a matching concave female end with an unlocking pin, enabling bidirectional torque transmission and axial tension transfer. Chamfered interfaces (10×5 mm and 20×10 mm) allow self-aligning assembly under angular deviations up to 10°, effectively reducing coupling precision requirements. Moreover, a lightweight YOLOv5-based detection model enhanced with a MultiScale Efficient Channel Attention (MSECA) module is developed for robust detection of key components (pin, box, lock, and unlock) under low-contrast and noisy underground conditions. Furthermore, a geometric projection-based method is introduced to calculate the relative angles of the pin and box, providing a reliable decision basis for intelligent alignment and disassembly. Experimental results verify the effectiveness of the proposed method: the maximum angle error for the pin is 5°, with an average of 2.9°, and for the box, the maximum is 8.9°, with an average of 6.1°; the overall maximum pin–box deviation is 8.8°. During unlocking, the box shows a maximum error of 4.2°, with an average of 2.85°. Coupling and disassembly tests further confirm the robustness and reliability of the proposed system. Coal mine drilling Drill pipe assembly and disassembly Target detection Angle measurement Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Jul, 2025 Reviewers invited by journal 24 Jul, 2025 Editor assigned by journal 23 Jul, 2025 Submission checks completed at journal 23 Jul, 2025 First submitted to journal 22 Jul, 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. 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-7186415","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490521553,"identity":"97e8af92-b78b-4494-9af4-dfe37df29338","order_by":0,"name":"Jiangnan Luo","email":"","orcid":"","institution":"Suzhou University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiangnan","middleName":"","lastName":"Luo","suffix":""},{"id":490521554,"identity":"72061592-7cd8-4b18-8302-b575b3f80a81","order_by":1,"name":"Deyi Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYDCCA0CUYCAhx8/MfPAB8VoeFNgYS7azJRsQrYXxwYe0xA3necwEiNLBd7zHEOiww8bGhxnMGBhqbKIJapE8c8YApEXO7DBD2gOGY2m5DYS0GNzIAWsxBmo5bsDYcJgILfffgLUkbm5mbJMgTssNHpAWoPeZmdmI0yJ5Jq0AqMXGWOIwG7NBAjF+4Tt+ePPHH3+AUdl//uODDzU2hLUwMHAgRWACYeUgwP6AOHWjYBSMglEwcgEAGcFG7qyB36oAAAAASUVORK5CYII=","orcid":"","institution":"Suzhou University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Deyi","middleName":"","lastName":"Zhang","suffix":""},{"id":490521557,"identity":"da132da0-9a80-43ad-92cd-fafc9a7dea0a","order_by":2,"name":"Yixiang Xu","email":"","orcid":"","institution":"Suzhou University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yixiang","middleName":"","lastName":"Xu","suffix":""},{"id":490521558,"identity":"d5f94bf2-6e0a-429a-ba15-4d82efb44304","order_by":3,"name":"Jianping Li","email":"","orcid":"","institution":"China University of Mining and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jianping","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-07-22 11:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7186415/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7186415/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87716809,"identity":"7e10a52e-bb10-4085-85dd-80217abcfdb4","added_by":"auto","created_at":"2025-07-28 09:15:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3028283,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7186415/v1_covered_9b72c834-8cad-4138-a875-f5436536c538.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intelligent assembly and disassembly method for coal mine drill pipe based on object detection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Coal mine drilling, Drill pipe assembly and disassembly, Target detection, Angle measurement","lastPublishedDoi":"10.21203/rs.3.rs-7186415/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7186415/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe assembly and disassembly of drill pipes are crucial operations in coal mine drilling, and their automation is essential for both worker safety and operational efficiency. However, due to the complex connection mechanisms of drill pipes, the harsh mining environment, and the lack of relevant research, intelligent disassembly remains a significant challenge. This study proposes a novel quadrangular spiral drill pipe and an intelligent assembly and disassembly method. The drill pipe features a convex quadrangular prism male end with a locking pin and a matching concave female end with an unlocking pin, enabling bidirectional torque transmission and axial tension transfer. Chamfered interfaces (10×5 mm and 20×10 mm) allow self-aligning assembly under angular deviations up to 10°, effectively reducing coupling precision requirements. Moreover, a lightweight YOLOv5-based detection model enhanced with a MultiScale Efficient Channel Attention (MSECA) module is developed for robust detection of key components (pin, box, lock, and unlock) under low-contrast and noisy underground conditions. Furthermore, a geometric projection-based method is introduced to calculate the relative angles of the pin and box, providing a reliable decision basis for intelligent alignment and disassembly. Experimental results verify the effectiveness of the proposed method: the maximum angle error for the pin is 5°, with an average of 2.9°, and for the box, the maximum is 8.9°, with an average of 6.1°; the overall maximum pin–box deviation is 8.8°. During unlocking, the box shows a maximum error of 4.2°, with an average of 2.85°. 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