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This work applies gradient boosting machine learning methods to machining data to predict surface roughness, with numerical results validated through contour experiments. Turning trials used Taguchi’s L27 array to study depth of cut, feed rate, and spindle speed. Higher spindle speed, lower feed rate, and shallower cuts improved surface quality in turned jute/basalt epoxy composites. XGBoost achieved the lowest maximum and average prediction errors of 3.78% and 2.24%, respectively.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/epoxy-composite-reinforced-with-jutebasalt-hybrid-characterisation-and-performance-evaluation-using-machine-learning-techniques/291195/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/epoxy-composite-reinforced-with-jutebasalt-hybrid-characterisation-and-performance-evaluation-using-machine-learning-techniques/291195.png","ImageObject",300,407,{"name":92,"@type":93},"Valentina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of this study on jute/basalt epoxy composites?","Question",{"text":112,"@type":113},"To use machine learning—especially gradient boosting methods—to predict machining surface roughness and validate numerical predictions using experimental contour results.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which machining parameters were varied in the turning experiments?",{"text":117,"@type":113},"Depth of cut, feed rate, and spindle speed were varied using Taguchi’s L27 orthogonal array.",{"name":119,"@type":110,"acceptedAnswer":120},"How did the machine learning models compare in prediction accuracy?",{"text":121,"@type":113},"XGBoost outperformed GBM and AdaBoost, with maximum and average prediction errors of 3.78% and 2.24% respectively.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},291195,1789643676,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":36,"preview_page_count":4},13056703020460,"https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923","Composites Part C: Open Access 14 (2024) 100453  \nContents lists available at ScienceDirect  \nComposites Part C: Open Access  \njournal [homepage:](homepage: www.sciencedirect.com/journal/composites-part-c-open-access)[ www.sciencedirect.com/journal/composites-part-c-open-access](homepage: www.sciencedirect.com/journal/composites-part-c-open-access)  \n| Epoxy composite reinforced with jute/basalt hybrid – Characterisation and   performance evaluation using machine learning techniques\u003Cbr>Amith Gadagi a, Baskaran Sivaprakash b, Chandrashekar Adake a, Umesh Deshannavar c, Prasad G. Hegde a, Santhosh P ․ d, *, Natarajan Rajamohan e, Ahmed I. Osman f\u003Cbr>a Department of Mechanical Engineering, KLE Dr. M. S. Sheshgiri College of Engineering and Technology, Belagavi, Karnataka, India b TamilNadu Government Polytechnic, Madurai, India\u003Cbr>c Department of Chemical Engineering, Tatyasaheb Kore College of Engineering and Technology, Warananagar, India d Department of Electrical and Electronic Engineering,University of Cagliari, Italy\u003Cbr>e Chemical Engineering Section, Faculty of Engineering, Sohar University, Sohar, Oman\u003Cbr>f School of Chemistry and Chemical Engineering, Queen’s University Belfast, Belfast BT9 5AG, Northern Ireland, UK |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Biofibre;POlymer;OPtimisation Machine learning Characterisation\u003Cbr>Xgboost |  | Epoxy resins, prized for their versatile properties, are derived from bio-based materials, contributing to sustainability and eco-friendliness in both production and application. This study focuses on the application of gradient boosting machine learning techniques in the field of machining to predict the surface roughness and also the contour based experimental validation of the numerical results. The turning experiments, conducted via Taguchi’s L27 array, aimed to explore the effects of depth of cut, feed rate, and spindle speed. Higher spindle speeds, lower feed rates, and shallower cuts led to smoother surfaces in turned jute/basalt epoxy composites. Machine learning models (Gradient Boosting Machine, AdaBoost, and XGBoost) were then used to predict surface roughness. Amongst these, XGBoost outperformed GBM and AdaBoost, exhibiting maximum and average prediction errors of 3.78 % and 2.24 %, respectively. XGBoost accurately predicted 2D surface roughness contours that closely matched experimental contours for training and test cases. Taguchi’s Orthogonal Matrix identified minimum surface roughness values as 0.773 μm (experimental), 0.800 μm (GBM), 0.880 μm (AdaBoost), and 0.774 μm (XGBoost). All were achieved at 1500 rpm spindle speed, 0.05 mm/rev feed rate, and 0.3 mm depth of cut. |\n\n1. Introduction  \nComposites that integrate natural fibres with a polymer matrix or alternative binders define a distinct material category. Amongst these, jute fibres stand out for their affordability, abundance, and commendable mechanical properties. Widely employed across multiple industries-from textiles, construction, cosmetics, and medicine to packaging, automotive, and furniture-jute-based composites are derived from renewable sources, exhibiting biodegradability and a significantly reduced environmental impact. Their exceptional features, including sustainability, excellent insulation, and high tensile strength, have mainly propelled their extensive use in automotive and aerospace applications in recent times [1,2]. Epoxy resins, belonging to the prepolymer and polymer group, yield robust and versatile materials widely applied across various commercial and industrial sectors.  \nJute-basalt epoxy hybrids are popular because of their exceptional strength, eco-friendliness, and biodegradability. Basalt, known for its high young’s modulus and tensile strength, coupled with jute’s excellent anti-static properties, enhances these composites resistance even at elevated temperatures, making them ideal for heat or fire exposure applications. Moreover, the syn","cbCaip4BchsjcTOc","https://ap.wps.com/l/cbCaip4BchsjcTOc","pdf",914968,16,"English","# Introduction\n## Machine learning for machining performance\n## Turning experiment design (Taguchi L27)\n## Surface roughness prediction and contour validation","[{\"question\":\"What is the main goal of this study on jute/basalt epoxy composites?\",\"answer\":\"To use machine learning—especially gradient boosting methods—to predict machining surface roughness and validate numerical predictions using experimental contour results.\"},{\"question\":\"Which machining parameters were varied in the turning experiments?\",\"answer\":\"Depth of cut, feed rate, and spindle speed were varied using Taguchi’s L27 orthogonal array.\"},{\"question\":\"How did the machine learning models compare in prediction accuracy?\",\"answer\":\"XGBoost outperformed GBM and AdaBoost, with maximum and average prediction errors of 3.78% and 2.24% respectively.\"}]","Epoxy composite reinforced with jute/basalt hybrid - Characterisation and performance evaluation using machine learning techniques | PDF"]