[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118270-en":3,"doc-seo-118270-105":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},118270,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","SELECTION OF MACHINE LEARNING ALGORITHMS FOR NANOCOMPOSITE ZA-27 MATERIAL TRANSFER PREDICTION - read online free","Study investigates machine learning methods for predicting material transfer in tribological contacts involving nanocomposite ZA-27 alloy matrix reinforced with Al2O3 at 1%, 3%, and 5% volume fractions. Results show ML models can accurately predict whether material transfer occurs, with the Gradient Boosting Classifier achieving the best predictive accuracy. Findings indicate practical value for predicting and preventing transfer to improve reliability and durability, emphasizing the role of domain expertise in algorithm and feature selection.","| \u003Cbr>Serbian Tribology Society | SERBIATRIB ‘23\u003Cbr>18th International Conference on\u003Cbr>Tribology | \u003Cbr>Faculty of Engineering University of Kragujevac |\n| --- | --- | --- |\n| Kragujevac, Serbia, 17 – 19 May 2023 |  |  |\n\nSELECTION OF MACHINE LEARNING ALGORITHMS FOR NANOCOMPOSITE ZA-27 MATERIAL TRANSFER PREDICTION  \nAleksandar ĐORĐEVIĆ1, Dragan DŽUNIĆ1, Marko PANTIĆ2, *, Milan ERIĆ1, Slobodan MITROVIĆ1,  \nMiladin STEFANOVIĆ1  \n1 University of Kragujevac, Faculty of Engineering, Kragujevac, Serbia  \n2 University of Priština in Kosovska Mitrovica, Faculty of Technical Sciences, Kosovska Mitrovica, Serbia  \n*[Corresponding author: marko.pantic@pr.ac.rs](Corresponding author: marko.pantic@pr.ac.rs)  \nAbstract: This study explores the use of machine learning algorithms in predicting material transfer in tribological contacts. The results of the analysis indicate that the machine learning models can accurately predict the occurrence of material transfer with a high degree of accuracy. The Gradient Boosting Classifier algorithm was found to outperform other algorithms in terms of predictive accuracy. The study's practical implications suggest that machine learning can be an effective tool for predicting and preventing material transfer, leading to increased system reliability and durability. The findings highlight the importance of domain-specific expertise in selecting appropriate algorithms and input features. One limitation of the study is that it focused only on material transfer and did not consider other important factors such as wear and friction. Future research could investigate the use of machine learning algorithms in predicting wear and friction in tribological systems.  \nKeywords: machine learning, data-driven analyses, machine learning hiperparameter optimization, Triboinformatics.  \n1. INTRODUCTION  \nIn the last decade, the use of “Big Data” and data-driven analysis in many scientific and engineering fields has become the prevailing trend. Tribology, the science of surface interactions, as it has been and continues to be one of the most relevant scientific and engineering disciplines is not devoid of this trend. When it comes totribology, large amounts of data are generated as a result of a large number of experiments, with parameters such as friction and wear, whose values are constantly measured. Furthermore, since friction and wear have a  \ncomplex nature, tribology has remained a highly experimental and empirical science, leading to the formation of big data on friction, wear, and surface properties of different materials, that postsan opening to perform data-driven analyses. The appearance of contemporary computer systems with significant computing power enabled the rapid development of new approaches to data-driven analysis that originated new insights. “Big Data” algorithms belonging to Artificial Intelligence (AI) and machine learning (ML) are now being used to determine new correlations within data-driven areas that cannot otherwise be discovered using  \ntraditional methods [1] . The application of new data-driven approaches paved the way for the development of new areas within tribology such as \"Triboinformatics\" and \"Intelligent tribology\"  \n[2] .  \nThe characteristic that was used to perform data-driven analyses in this paper is material transfer from different nanocompozite of ZA-27 alloy matrix reinforced with different volume fractions of nanometric \"Al2O3\", i.e. \"1%Al2O3\",\"3%Al2O3\", and \"5%Al2O3\", respectively. These materials were used because, as a consequence of its higher chemical reactivity, aluminium bonds to steel much more quickly during sliding, leading to adhesive wear and material transfer [3] .  \nAlthough many factors, including surface roughness, lubricants, sliding distance, contact pressure, and tool coating, are known to influence the occurrence of material transfer at low temperatures (i.e. room temperature), describing the mechanisms behind the initiation of material transfer remains a","cbCaic3LiyYOtZZL","https://ap.wps.com/l/cbCaic3LiyYOtZZL","pdf",1033985,1,10,"English","en",105,"# Abstract\n# Keywords\n# 1. Introduction","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To evaluate machine learning algorithms for predicting material transfer in tribological contacts for a nanocomposite ZA-27 material system reinforced with Al2O3.\"},{\"question\":\"Which algorithm performed best for material transfer prediction?\",\"answer\":\"The Gradient Boosting Classifier showed the highest predictive accuracy compared with other evaluated algorithms.\"},{\"question\":\"Why are ZA-27 and Al2O3 used in the experiments?\",\"answer\":\"Due to aluminium’s higher chemical reactivity that bonds quickly to steel during sliding, influencing adhesive wear and material transfer; Al2O3 additions are used to improve tribological behavior such as wear resistance and friction reduction.\"}]","SELECTION OF MACHINE LEARNING ALGORITHMS FOR NANOCOMPOSITE ZA-27 MATERIAL TRANSFER PREDICTION - read online free | PDF",1785682736,25,{"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},"selection-of-machine-learning-algorithms-for-nanocomposite-za-27-material-transfer-prediction-read-online-free","",{"@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/selection-of-machine-learning-algorithms-for-nanocomposite-za-27-material-transfer-prediction-read-online-free/118270/",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","2026-08-02",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},"What is the main objective of the study?","Question",{"text":75,"@type":76},"To evaluate machine learning algorithms for predicting material transfer in tribological contacts for a nanocomposite ZA-27 material system reinforced with Al2O3.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which algorithm performed best for material transfer prediction?",{"text":80,"@type":76},"The Gradient Boosting Classifier showed the highest predictive accuracy compared with other evaluated algorithms.",{"name":82,"@type":73,"acceptedAnswer":83},"Why are ZA-27 and Al2O3 used in the experiments?",{"text":84,"@type":76},"Due to aluminium’s higher chemical reactivity that bonds quickly to steel during sliding, influencing adhesive wear and material transfer; Al2O3 additions are used to improve tribological behavior such as wear resistance and friction reduction.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]