Machine Learning Approach for Process Optimization of Black Nickel Electroplating

Machine Learning Approach for Process Optimization of Black Nickel Electroplating | 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 Machine Learning Approach for Process Optimization of Black Nickel Electroplating YAJUAN SUN, Yong Teck Tan, Yang Zhao, Aaron Teo, Yujie Zhou, Joseph Wong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4020468/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Nov, 2024 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract Optimization of electroplating processes is crucial yet time-consuming. In an electroplating process like black nickel plating, achieving a consistent coating becomes challenging without boric acid, which serves as a pH buffer and widens the operational process window. Despite its benefits, the use of boric acid conflicts with the growing emphasis on sustainable and eco-friendly manufacturing. This complication intensifies the intricacy of process optimization. To tackle this challenge, this study employs machine learning techniques to gain insights into optimizing a boric acid-free black nickel plating process. Specifically, three machine learning models were developed to predict coating defects, coating colour, and coating mass. Drawing from the predictions of these models, an optimization algorithm is proposed to select process parameters ensuring a flawless coating with the desired colour and mass, which is validated by experimental result. The machine learning models were able to give accurate predictions for black nickel electroplating process which behaves in a non-linear manner. This research illustrates how machine learning can enhance the manufacturing industry, particularly in the surface finishing sector, by providing an effective approach to optimize processes in non-linear scenarios, thereby improving product quality and work productivity. Black nickel plating Process optimization Machine learning Electroplating Coating Full Text Cite Share Download PDF Status: Published Journal Publication published 13 Nov, 2024 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 01 Jul, 2024 Reviewers agreed at journal 25 Mar, 2024 Reviewers invited by journal 18 Mar, 2024 Editor assigned by journal 07 Mar, 2024 First submitted to journal 06 Mar, 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-4020468","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281090740,"identity":"af4d0e51-bf8e-49fd-a01b-d6abb26e7279","order_by":0,"name":"YAJUAN 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In an electroplating process like black nickel plating, achieving a consistent coating becomes challenging without boric acid, which serves as a pH buffer and widens the operational process window. Despite its benefits, the use of boric acid conflicts with the growing emphasis on sustainable and eco-friendly manufacturing. This complication intensifies the intricacy of process optimization. To tackle this challenge, this study employs machine learning techniques to gain insights into optimizing a boric acid-free black nickel plating process. Specifically, three machine learning models were developed to predict coating defects, coating colour, and coating mass. Drawing from the predictions\nof these models, an optimization algorithm is proposed to select process parameters ensuring a flawless coating with the desired colour and mass, which is validated by experimental result. 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This research illustrates how machine learning can enhance the manufacturing industry, particularly in the surface finishing sector, by providing an effective approach to optimize processes in non-linear scenarios, thereby improving product quality and work productivity.","manuscriptTitle":"Machine Learning Approach for Process Optimization of Black Nickel Electroplating","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 15:29:54","doi":"10.21203/rs.3.rs-4020468/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2024-07-01T12:51:44+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-03-25T10:37:42+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-18T21:35:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-08T01:19:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2024-03-06T05:42:48+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":"ddb40f03-aed5-4005-9d7a-44ec62250d8a","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-18T19:22:08+00:00","versionOfRecord":{"articleIdentity":"rs-4020468","link":"https://doi.org/10.1007/s00170-024-14606-4","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2024-11-13 15:57:26","publishedOnDateReadable":"November 13th, 2024"},"versionCreatedAt":"2024-03-21 15:29:54","video":"","vorDoi":"10.1007/s00170-024-14606-4","vorDoiUrl":"https://doi.org/10.1007/s00170-024-14606-4","workflowStages":[]},"version":"v1","identity":"rs-4020468","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4020468","identity":"rs-4020468","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}