A Machine Vision-Based Measurement Method for the Concentricity of Automotive Brake Piston Components

A Machine Vision-Based Measurement Method for the Concentricity of Automotive Brake Piston Components | 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 A Machine Vision-Based Measurement Method for the Concentricity of Automotive Brake Piston Components Ge Weinan, Li Qinghua, Zhao Wanting, Xu Tiantian, Zhang Shihong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4889379/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 The stability and reliability of the brake system are critically affected by the concentricity error of automotive brake piston components. Traditional contact-based concentricity measurement methods are inefficient. To address the issue of low detection efficiency, a non-contact concentricity measurement method based on the combination of machine vision and image processing technology is proposed in this paper. In this method, an industrial camera is utilized to capture images of the measured part's end face from the top of the spring. Edge contours are extracted through image preprocessing algorithms, the outer circle center is calculated, and the inner circle center is fitted. Finally, the concentricity error is calculated using the coordinates of the two circle centers. Experimental results show that, compared to a coordinate measuring machine(CMM), this method has a maximum error of only 0.0393mm and an average measurement time of just 3.9s. It significantly improves measurement efficiency and meets the industry's demand for automated inspection. The experiments verified the feasibility and effectiveness of this method in practical engineering applications, providing reliable technical support for the online inspection of automotive brake piston components. Additionally, this method can be applied to the concentricity measurement of other complex stepped shaft parts. Industrial Engineering Automotive brake piston components Concentricity measurement Machine vision Image processing Least squares method Full Text Additional Declarations The authors declare no competing interests. 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-4889379","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338346546,"identity":"0aeb2288-1cc9-428e-ad4f-6e04ed007093","order_by":0,"name":"Ge Weinan","email":"","orcid":"","institution":"Changchun Guanghua University","correspondingAuthor":false,"prefix":"","firstName":"Ge","middleName":"","lastName":"Weinan","suffix":""},{"id":338346547,"identity":"f74228ab-1e09-493a-9dad-59f8333c2f87","order_by":1,"name":"Li Qinghua","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIie3QMWrDMBSA4ScE6qKgVaYlZ3hjoQZfRaHgLjV4Cp2CIVAvOYAhl2gwZJbR0MVJ1gQ6ZO+i0UMLVdJ4qhpCpw76EebJ8CHZAKHQP0xQUmiFkourAkDl/Xv2O4lK2mibxzfRTDuCFxDcsPumsmmMW3XYXkDAcDwu2L2P7B4nIMoWwY4NiHnhFWR6ImSe1VKhAdk+IqnWbnjTXkLpidDr7MURDe5XIB08GzcoL2E9YdGq7pS72JF8niGcMtVUmHIuB0t3Cv0m5AyRlGptMeaSZ8tb9y1ctmnezNYPXG79JNk0U6s+ZJK8rupd9zQZitIs9t34bigqP/l508ND90MoFAqF/tYX0GhZ8Ecuon4AAAAASUVORK5CYII=","orcid":"","institution":"Changchun University","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Qinghua","suffix":""},{"id":338346548,"identity":"b38bbf9c-94ed-454c-bddb-10e3ac869a4b","order_by":2,"name":"Zhao Wanting","email":"","orcid":"","institution":"Changchun University","correspondingAuthor":false,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Wanting","suffix":""},{"id":338346549,"identity":"77839531-268d-4f8a-94a6-38d84d6fe9fc","order_by":3,"name":"Xu Tiantian","email":"","orcid":"","institution":"Changchun University","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Tiantian","suffix":""},{"id":338346550,"identity":"7348279b-ac16-47b4-8ded-ee59b057b4bd","order_by":4,"name":"Zhang Shihong","email":"","orcid":"","institution":"Changchun University","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Shihong","suffix":""}],"badges":[],"createdAt":"2024-08-10 00:16:11","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4889379/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4889379/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62320772,"identity":"9dc332ec-bda5-4297-baef-de0534e02098","added_by":"auto","created_at":"2024-08-13 01:38:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1015684,"visible":true,"origin":"","legend":"","description":"","filename":"geweinanManuscriptFile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4889379/v1_covered_2d8d72ec-9cfd-4415-aece-cefe455780cb.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA Machine Vision-Based Measurement Method for the Concentricity of Automotive Brake Piston Components\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Jilin Province Science and Technology Department","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"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":"Automotive brake piston components, Concentricity measurement, Machine vision, Image processing, Least squares method","lastPublishedDoi":"10.21203/rs.3.rs-4889379/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4889379/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe stability and reliability of the brake system are critically affected by the concentricity error of automotive brake piston components. Traditional contact-based concentricity measurement methods are inefficient. To address the issue of low detection efficiency, a non-contact concentricity measurement method based on the combination of machine vision and image processing technology is proposed in this paper. In this method, an industrial camera is utilized to capture images of the measured part's end face from the top of the spring. Edge contours are extracted through image preprocessing algorithms, the outer circle center is calculated, and the inner circle center is fitted. Finally, the concentricity error is calculated using the coordinates of the two circle centers. Experimental results show that, compared to a coordinate measuring machine(CMM), this method has a maximum error of only 0.0393mm and an average measurement time of just 3.9s. It significantly improves measurement efficiency and meets the industry's demand for automated inspection. The experiments verified the feasibility and effectiveness of this method in practical engineering applications, providing reliable technical support for the online inspection of automotive brake piston components. Additionally, this method can be applied to the concentricity measurement of other complex stepped shaft parts.\u003c/p\u003e","manuscriptTitle":"A Machine Vision-Based Measurement Method for the Concentricity of Automotive Brake Piston Components","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-13 01:30:18","doi":"10.21203/rs.3.rs-4889379/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"fa4c4e3d-a66f-4c99-b16b-c33ea4f649e1","owner":[],"postedDate":"August 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35849433,"name":"Industrial Engineering"}],"tags":[],"updatedAt":"2024-08-13T01:30:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-13 01:30:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4889379","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4889379","identity":"rs-4889379","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}