Enhancing Grinding Efficiency in Aluminum Alloys: An Ensemble‑Stacking and Single Machine Learning Framework for Predicting Surface Roughness with SHAP-based interpretability

Enhancing Grinding Efficiency in Aluminum Alloys: An Ensemble‑Stacking and Single Machine Learning Framework for Predicting Surface Roughness with SHAP-based interpretability | 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 Enhancing Grinding Efficiency in Aluminum Alloys: An Ensemble‑Stacking and Single Machine Learning Framework for Predicting Surface Roughness with SHAP-based interpretability Mohsen Dehghanpour Abyaneh, Mohammad Sadegh Javadi, Parviz Narimani, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7647149/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Apr, 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 The digitalization of mechanical engineering requires reliable prediction tools that are interpretable and facilitate faster analysis. The study applied benchmarks for data-driven strategies and conducted a comprehensive analysis, using an 84-run grinding dataset on aluminum alloy 6061 that includes three different grinding wheel types, four different coolants, and seven specific removal rates with the surface roughness as the output variable. Particle Swarm Optimization was the first applied and tuned using a closed-form formula, but with unreliable accuracy metrics. Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGB) were the machine learning algorithms that were tested in predicting the relationship between input variables and the output. The best single model performance was given by GPR with an accuracy of 97.50%, a MAPE of 2.49% and an R 2 of 0.99. There were also three models of stacking ensembles that were applied. The stacking ensemble using ANN and XGB as base learners and GPR as the meta-learner offered the best trade-off between its bias and variance and achieved an overall accuracy of 94.54, a MAPE of 5.45, and an R 2 of 0.98. The sensitivity analysis was employed to assess the significance of input parameters. Shapley Additive Explanations (SHAP) was also used to give attribution to each case to attribute the impact of individual input features to each prediction. Among the variables, grinding wheel type 89A180K6V111 and the specific removal rate were the most influential. These novel stacking-ensemble and analysis methods can be broadened for future applicability. Grinding. Aluminum alloy 6061. Surface roughness. Stacking ensemble. Shapley Additive Explanations Full Text Supplementary Files Onlinefloatimage1.png Cite Share Download PDF Status: Published Journal Publication published 10 Apr, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 19 Nov, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers invited by journal 07 Oct, 2025 Editor assigned by journal 23 Sep, 2025 First submitted to journal 22 Sep, 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-7647149","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":525868485,"identity":"97b0b331-5d74-4f9b-a202-4c76ed2f357b","order_by":0,"name":"Mohsen Dehghanpour 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Aluminum alloy 6061. Surface roughness. Stacking ensemble. Shapley Additive Explanations","lastPublishedDoi":"10.21203/rs.3.rs-7647149/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7647149/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe digitalization of mechanical engineering requires reliable prediction tools that are interpretable and facilitate faster analysis. The study applied benchmarks for data-driven strategies and conducted a comprehensive analysis, using an 84-run grinding dataset on aluminum alloy 6061 that includes three different grinding wheel types, four different coolants, and seven specific removal rates with the surface roughness as the output variable. Particle Swarm Optimization was the first applied and tuned using a closed-form formula, but with unreliable accuracy metrics. Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGB) were the machine learning algorithms that were tested in predicting the relationship between input variables and the output. The best single model performance was given by GPR with an accuracy of 97.50%, a MAPE of 2.49% and an R\u003csup\u003e2\u003c/sup\u003e of 0.99. There were also three models of stacking ensembles that were applied. The stacking ensemble using ANN and XGB as base learners and GPR as the meta-learner offered the best trade-off between its bias and variance and achieved an overall accuracy of 94.54, a MAPE of 5.45, and an R\u003csup\u003e2\u003c/sup\u003e of 0.98. The sensitivity analysis was employed to assess the significance of input parameters. Shapley Additive Explanations (SHAP) was also used to give attribution to each case to attribute the impact of individual input features to each prediction. Among the variables, grinding wheel type 89A180K6V111 and the specific removal rate were the most influential. These novel stacking-ensemble and analysis methods can be broadened for future applicability.\u003c/p\u003e","manuscriptTitle":"Enhancing Grinding Efficiency in Aluminum Alloys: An Ensemble‑Stacking and Single Machine Learning Framework for Predicting Surface Roughness with SHAP-based interpretability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 18:36:26","doi":"10.21203/rs.3.rs-7647149/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2025-11-19T13:04:02+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-10-09T01:18:58+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-07T09:42:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-23T05:45:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2025-09-22T08:42:00+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":"229d6fda-a4a8-4d90-9a20-ea3ad683055c","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:11:29+00:00","versionOfRecord":{"articleIdentity":"rs-7647149","link":"https://doi.org/10.1007/s00170-026-17594-9","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2026-04-10 15:58:51","publishedOnDateReadable":"April 10th, 2026"},"versionCreatedAt":"2025-10-20 18:36:26","video":"","vorDoi":"10.1007/s00170-026-17594-9","vorDoiUrl":"https://doi.org/10.1007/s00170-026-17594-9","workflowStages":[]},"version":"v1","identity":"rs-7647149","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7647149","identity":"rs-7647149","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}