[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119977-en":3,"doc-seo-119977-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119977,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","A Generalised Method for Friction Optimisation of Surface Textured Seals by Machine Learning - Article","Friction behaviour is a key property of dynamic seals, and surface texturing offers an effective way to tune friction without changing materials or lubricants. However, translating manual predictions of optimal friction-reducing textures into practice is difficult. This work employs machine learning to predict optimal texture parameters using experimentally measured dimple geometry and measured friction data across lubrication regimes, guided end-to-end by a generalised modelling workflow. A large set of regression algorithms is evaluated with tribological metrics such as Stribeck curves, and the selected model predicts low-friction parameter combinations over a velocity interval.","lubricants  \nArticle  \nA Generalised Method for Friction Optimisation of Surface Textured Seals by Machine Learning  \nMarkus Brase *,†, Jonathan Binder †, Mirco Jonkeren  and Matthias Wangenheim   \nCitation: Brase, M.; Binder, J.; Jonkeren, M.; Wangenheim, M. A Generalised Method for Friction Optimisation of Surface Textured Seals by Machine Learning. Lubricants 2024, 12, 20. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/lubricants12010020](10.3390/lubricants12010020)  \nReceived: 2 November 2023  \nRevised: 14 December 2023  \nAccepted: 26 December 2023  \nPublished: 9 January 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nInstitute of Dynamics and Vibration Research, Leibniz Universität Hannover, 30823 Garbsen, Germany;  \n[jonkeren@ids.uni-hannover.de](jonkeren@ids.uni-hannover.de) (M.J.); [wangenheim@ids.uni-hannover.de](wangenheim@ids.uni-hannover.de) (M.W.)  \n* Correspondence: brase@ids.uni-hannover.de † These authors contributed equally to this work.  \nAbstract: Friction behaviour is an important characteristic of dynamic seals. Surface texturing isan effective method to control the friction level without the need to change materials or lubricants. However, it is difficult to put the manual prediction of optimal friction reducing textures as a function of operating conditions into practice. Therefore, in this paper, we use machine learning techniques for the prediction of optimal texture parameters for friction optimisation. The application of pneumatic piston seals serves as an illustrative example to demonstrate the machine learning method and results. The analyses of this work are based on experimentally determined data of surface texture parameters, defined by the dimple diameter, distance, and depth. Furthermore friction data between the seal and the pneumatic cylinder are measured in different friction regimes from boundary over mixed up to hydrodynamic lubrication. A particular innovation of this work is the definition of a generalised method that guides the entire machine learning process from raw data acquisition to model prediction, without committing to only a few learning algorithms. A large number of 26 regression learning algorithms are used to build machine learning models through supervised learning to evaluate the suitability of different models in the specific application context. In order to select the best model, mathematical metrics and tribological relationships, like Stribeck curves, are applied and compared with each other. The resulting model is utilised in the subsequent friction optimisation step, in which optimal surface texture parameter combinations with the lowest friction coefficients are predicted over a defined interval of relative velocities. Finally, the friction behaviour is evaluated in the context of the model and optimal value combinations of the surface texture parameters are identified for different lubrication conditions.  \nKeywords: supervised learning; regression techniques; surface texturing; dynamic seals  \n1. Introduction  \nFriction is defined as the force of resistance acting between the contact surfaces of bodies in relative motion [1] . In total, about 20% of global energy losses are due to overcoming friction [2] . Therefore, low friction is targeted in many technical systems such as seals or bearings. In order to reduce friction in tribological systems, it is necessary to understand the individual factors that influence friction and to develop appropriate strategies to minimise the friction [1] .  \nOn the one hand, material properties, such as the crystal structure [3], hardness [4,5], elastic and shear modulus [6,7], grain size [8,9], and","cbCaicugnHOPaJgK","https://ap.wps.com/l/cbCaicugnHOPaJgK","pdf",9923530,1,19,"English","en",105,"# Introduction\n## Friction and its importance in tribological systems\n## Factors influencing friction: materials and operating conditions\n## Surface modifications and texturing of contacting surfaces\n## Aim of the work and application context (surface textured seals)","[{\"question\":\"Why is friction optimisation important for dynamic seals?\",\"answer\":\"Friction directly affects the performance of seals in relative motion. Lower friction reduces resistance losses and supports efficient operation in tribological systems.\"},{\"question\":\"How does surface texturing help reduce friction?\",\"answer\":\"Surface texturing modifies the seal surface topography to control the friction level without requiring changes to materials or lubricants. Textures are defined through dimple geometry such as diameter, distance, and depth.\"},{\"question\":\"What role does machine learning play in the method?\",\"answer\":\"Machine learning predicts optimal texture parameters for friction optimisation using supervised regression models trained on experimentally determined texture parameters and measured friction data across different lubrication regimes.\"}]","A Generalised Method for Friction Optimisation of Surface Textured Seals by Machine Learning - Article | PDF",1785727407,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-generalised-method-for-friction-optimization-of-surface-textured-seals-by-machine-learning-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-generalised-method-for-friction-optimization-of-surface-textured-seals-by-machine-learning-article/119977/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is friction optimisation important for dynamic seals?","Question",{"text":75,"@type":76},"Friction directly affects the performance of seals in relative motion. Lower friction reduces resistance losses and supports efficient operation in tribological systems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does surface texturing help reduce friction?",{"text":80,"@type":76},"Surface texturing modifies the seal surface topography to control the friction level without requiring changes to materials or lubricants. Textures are defined through dimple geometry such as diameter, distance, and depth.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does machine learning play in the method?",{"text":84,"@type":76},"Machine learning predicts optimal texture parameters for friction optimisation using supervised regression models trained on experimentally determined texture parameters and measured friction data across different lubrication regimes.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]