Application of Machine Learning for Predicting the Incubation Period of Water Droplet Erosion in Metals

Application of Machine Learning for Predicting the Incubation Period of Water Droplet Erosion in Metals | 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 Application of Machine Learning for Predicting the Incubation Period of Water Droplet Erosion in Metals Khaled AlHammad, Mamoun Medraj, Moussa Tembely This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6180419/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Water droplet erosion (WDE) is a critical phenomenon that leads to material degradation in many engineering applications, particularly in power generation and aerospace industry. Accurate prediction of the incubation period is essential for optimizing material selection and maintenance strategies. Traditional empirical models, while helpful, often lack predictive accuracy due to their reliance on numerous parameters with limited physical interpretation. In this study, a machine learning (ML) approach was developed to predict the incubation period for different materials. A range of ML models—including linear regression (LR), decision tree regressor (DT), random forest regressor (RF), gradient boosting regressor (GBR), and artificial neural networks (ANN)—was employed to capture the complex relationships between material properties and erosion conditions. Despite hyperparameter optimization using techniques such as grid search, no substantial improvement in model predictions was observed. Data transformation methods—logarithmic, Yeo-Johnson, and Box-Cox transformations—were applied to enhance model performance. A dataset derived from experimental measurements on five different alloys was used to train and validate the ML models. The results indicate that the ML models significantly outperform conventional empirical approaches. Notably, the LR model with Box-Cox transformation achieved an R² (coefficient of determination) of over 90% and low mean absolute error (MAE), while the ANN model with Yeo-Johnson transformation attained an R² above 85% with a correspondingly low MAE. Additionally, feature impact and importance analyses provided insights into the key factors influencing the duration of the incubation period, further validating the robustness of the models. This study offers a robust tool for predicting the incubation period of WDE, with broad applicability in engineering design and material selection across various industries. Water droplet erosion Machine learning Incubation period Prediction models Material degradation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Apr, 2025 Reviews received at journal 06 Apr, 2025 Reviews received at journal 04 Apr, 2025 Reviews received at journal 03 Apr, 2025 Reviews received at journal 03 Apr, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviewers agreed at journal 02 Apr, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers invited by journal 28 Mar, 2025 Editor assigned by journal 20 Mar, 2025 Submission checks completed at journal 20 Mar, 2025 First submitted to journal 07 Mar, 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. 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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-6180419","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":440528604,"identity":"09e760ad-a672-4973-87bc-751cc535085d","order_by":0,"name":"Khaled AlHammad","email":"","orcid":"","institution":"Concordia University","correspondingAuthor":false,"prefix":"","firstName":"Khaled","middleName":"","lastName":"AlHammad","suffix":""},{"id":440528607,"identity":"ca13ea67-6d34-4736-949f-3709326ca12e","order_by":1,"name":"Mamoun Medraj","email":"","orcid":"","institution":"Concordia University","correspondingAuthor":false,"prefix":"","firstName":"Mamoun","middleName":"","lastName":"Medraj","suffix":""},{"id":440528611,"identity":"ae017df3-efb6-4aa3-a44e-ca93a2e30a12","order_by":2,"name":"Moussa Tembely","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYHADHgaGBww2UA6bBEH1EmAtCQxppGs5DNOCW6lue4/xxx81DHX8YmcPPkhsOy/PL9388HFFmYU8A/vhB9i0mJ05YybNc4xBQnJ2XrJBYtttw5lzjhkbnjknYdjAk2aAVcuNHDNmoDMkDG7nmEkAtSQY3Mhhk2xsk2BskGDApQXosH8MEva3c8x/JLadg2uxb5Bg/4BDi4EEbxvQFukcM4bEtgNwLYkNEjzYbTlzrEyat09CcsbtvGSJhHPJhjNnpBkbNpyTSG7jySnAquV48+aPP77Z8PPPzj344UOZnTy/RPLDhw1ldbb97Mc3YA1lCIBGHCNydOCJGmTwhzhlo2AUjIJRMLIAAEgtW8jPo2J0AAAAAElFTkSuQmCC","orcid":"","institution":"Concordia University","correspondingAuthor":true,"prefix":"","firstName":"Moussa","middleName":"","lastName":"Tembely","suffix":""}],"badges":[],"createdAt":"2025-03-07 18:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6180419/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6180419/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80697123,"identity":"2540043e-792a-45fb-b4cd-78548b8903be","added_by":"auto","created_at":"2025-04-16 07:04:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1982376,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptDRAFTupdatedmm.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6180419/v1_covered_540f166b-01b1-48a2-8b93-c73fa8a911ec.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of Machine Learning for Predicting the Incubation Period of Water Droplet Erosion in Metals","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Water droplet erosion, Machine learning, Incubation period, Prediction models, Material degradation","lastPublishedDoi":"10.21203/rs.3.rs-6180419/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6180419/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater droplet erosion (WDE) is a critical phenomenon that leads to material degradation in many engineering applications, particularly in power generation and aerospace industry. Accurate prediction of the incubation period is essential for optimizing material selection and maintenance strategies. Traditional empirical models, while helpful, often lack predictive accuracy due to their reliance on numerous parameters with limited physical interpretation. In this study, a machine learning (ML) approach was developed to predict the incubation period for different materials. A range of ML models\u0026mdash;including linear regression (LR), decision tree regressor (DT), random forest regressor (RF), gradient boosting regressor (GBR), and artificial neural networks (ANN)\u0026mdash;was employed to capture the complex relationships between material properties and erosion conditions. Despite hyperparameter optimization using techniques such as grid search, no substantial improvement in model predictions was observed. Data transformation methods\u0026mdash;logarithmic, Yeo-Johnson, and Box-Cox transformations\u0026mdash;were applied to enhance model performance. A dataset derived from experimental measurements on five different alloys was used to train and validate the ML models. The results indicate that the ML models significantly outperform conventional empirical approaches. Notably, the LR model with Box-Cox transformation achieved an R\u0026sup2; (coefficient of determination) of over 90% and low mean absolute error (MAE), while the ANN model with Yeo-Johnson transformation attained an R\u0026sup2; above 85% with a correspondingly low MAE. Additionally, feature impact and importance analyses provided insights into the key factors influencing the duration of the incubation period, further validating the robustness of the models. This study offers a robust tool for predicting the incubation period of WDE, with broad applicability in engineering design and material selection across various industries.\u003c/p\u003e","manuscriptTitle":"Application of Machine Learning for Predicting the Incubation Period of Water Droplet Erosion in Metals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-16 06:32:47","doi":"10.21203/rs.3.rs-6180419/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-09T09:15:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-06T17:57:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-04T18:22:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-04T03:19:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-03T08:45:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73449616775434554808460755777843663659","date":"2025-04-03T06:20:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121432905113460209211767996297337628017","date":"2025-04-02T04:20:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249131679623495999550508811565713768295","date":"2025-03-29T00:50:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202145050899238599661792753369454987855","date":"2025-03-28T06:43:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272530321616794508939045665303017866726","date":"2025-03-28T04:23:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-28T04:17:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-20T06:50:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-20T06:48:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2025-03-07T18:42:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10ffa00f-37c2-4b99-90aa-e5b1a5d8b156","owner":[],"postedDate":"April 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-04T11:08:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-16 06:32:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6180419","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6180419","identity":"rs-6180419","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}