[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126899-en":3,"doc-seo-126899-105":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},126899,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Improved carbide volume fraction estimation in as-cast HCCI alloys using machine learning techniques","An improved approach is presented for estimating carbide volume fraction (CVF) in as-cast High Chromium Cast Iron (HCCI) alloys using machine learning (ML). The work overcomes limitations of earlier CVF formulae derived from a narrow set of alloy compositions by compiling a comprehensive dataset of 320 alloy compositions drawn from 60 sources. ML models identify carbon (C), chromium (Cr), and molybdenum (Mo) as key factors. The resulting predictive model increases accuracy across a wider composition range, reducing reliance on time-consuming experimental procedures while enabling more efficient and reliable CVF determination.","Computational Materials Science 240 (2024) 113013  \nContents lists available at ScienceDirect  \nComputational Materials Science  \njournal [homepage:](homepage: www.elsevier.com/locate/commatsci)[ www.elsevier.com/locate/commatsci](homepage: www.elsevier.com/locate/commatsci)  \n| Full Length Article\u003Cbr>Improved carbide volume fraction estimation in as-cast HCCI alloys using machine learning techniques |  |  |  |\n| --- | --- | --- | --- |\n| U. Pranav Nayaka, 1, *, Martin Müllera, b, 1, Noah Quartz a, M. Agustina Guitar a, Frank Mücklicha, b\u003Cbr>a Department of Materials Science, Saarland University, Campus D3.3, D-66123 Saarbrücken, Germany b Material Engineering Center Saarland (MECS), Campus D3.3, D-66123 Saarbrücken, Germany |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Carbide volume fraction High chromium cast iron Machine learning Metallography Microstructure\u003Cbr>Phase quantification |  | An improved approach is presented for the estimation of carbide volume fraction (CVF) in as-cast High Chromium Cast Iron (HCCI) alloys using Machine Learning (ML) techniques. The limitations of existing formulae for CVF estimation in HCCI alloys, which relied on a limited number of alloy compositions, are addressed. A comprehensive dataset comprising 320 distinct alloy compositions from 60 different sources was compiled. ML models trained on this dataset revealed the significant influence of carbon (C), chromium (Cr), and molybdenum (Mo) on CVF determination. By leveraging ML algorithms, a predictive model was developed that offers enhanced accuracy in estimating CVF across a wider range of compositions. This ML-based approach provides researchers with a valuable tool for determining CVF in as-cast HCCI alloys, minimizing the need for resourceintensive and time-consuming experimental procedures. The results obtained demonstrate improved CVF estimation accuracy and broader applicability, thus facilitating more efficient and reliable CVF determination in HCCI alloys. |  |\n\n1. Introduction  \nHigh chromium cast irons (HCCIs) are a subset of abrasion-resistant white cast irons (WCIs), offering superior wear resistance and toughness within the WCI category [1,2]. These alloys, based on the Fe-Cr-C ternary system, typically contain 11–30 wt% chromium (Cr) and 2–4 wt% carbon (C) according to ASTM A532 standards [3], along with minor additions of molybdenum (Mo), nickel (Ni), copper (Cu), and manganese (Mn) [4]. Characterized by hard eutectic carbides (EC) dispersed in a modifiable matrix (austenite, ferrite, martensite), HCCIs can possess up to 50 % carbides by volume due to their wide compositional range [5–8]. The carbides exhibit a hardness range of 1200–1600 HV [9,10], contributing synergistically with the matrix to enhance both wear resistance and toughness. These properties make HCCIs suitable for various industrial applications, including ore crushers, ball mill liners, and grinding equipment [4,11,12].  \nExtensive research has been conducted by numerous researchers to identify the optimal carbide volume fraction (CVF) for enhancing  \nhardness and wear resistance, with some studies maintaining the matrix microstructure relatively unchanged [13–19]. However, an increase in hardness does not necessarily correlate with improved wear resistance [20]. Zum Gahr et al. [21] and Doǧan et al. [22] observed increased material hardness with higher CVF, but without a commensurate improvement in wear resistance. Notably, Doǧan et al. [22] noted a significant reduction in wear volume loss for a 26 wt% Cr WCI with an austenitic matrix and a CVF of 28 % compared to a 16 wt% Cr WCI with a pearlitic/bainitic matrix and a CVF of 45 %.  \nMoreover, in low-stress abrasion scenarios, where the abrasive was softer than the carbides but harder than the matrix, increasing CVF improved wear resistance [19,21,23]. Conversely, under high-stress or three-body abrasion, microcracking of carbide tips occurred, indicating a threshold beyond whi","cbCaioVyti660cvw","https://ap.wps.com/l/cbCaioVyti660cvw","pdf",2308996,1,9,"English","en",105,"# Introduction\n## Background on HCCI and carbide volume fraction\n## Relationship between CVF, hardness, and wear resistance\n## Challenges in predicting CVF for as-cast HCCI alloys\n## Prior experimental and formula-based CVF estimation","[{\"question\":\"What problem does the paper address in estimating carbide volume fraction (CVF) for as-cast HCCI alloys?\",\"answer\":\"Existing CVF estimation formulae for HCCI rely on limited alloy composition sets, which restrict accuracy when applied more broadly. The paper proposes an ML-based method to improve predictive reliability across wider compositions.\"},{\"question\":\"How was the dataset for machine learning constructed?\",\"answer\":\"A comprehensive dataset with 320 distinct alloy compositions was compiled from 60 different sources, enabling model training across varied chemical inputs and CVF outcomes.\"},{\"question\":\"Which alloying elements are identified as significantly influencing CVF determination?\",\"answer\":\"The ML results indicate that carbon (C), chromium (Cr), and molybdenum (Mo) have significant influence on CVF determination.\"}]","Improved carbide volume fraction estimation in as-cast HCCI alloys using machine learning techniques | PDF",1785935502,23,{"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},"improved-carbide-volume-fraction-estimation-in-as-cast-hcci-alloys-using-machine-learning-techniques","",{"@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/improved-carbide-volume-fraction-estimation-in-as-cast-hcci-alloys-using-machine-learning-techniques/126899/",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","2026-08-05",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 problem does the paper address in estimating carbide volume fraction (CVF) for as-cast HCCI alloys?","Question",{"text":75,"@type":76},"Existing CVF estimation formulae for HCCI rely on limited alloy composition sets, which restrict accuracy when applied more broadly. The paper proposes an ML-based method to improve predictive reliability across wider compositions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the dataset for machine learning constructed?",{"text":80,"@type":76},"A comprehensive dataset with 320 distinct alloy compositions was compiled from 60 different sources, enabling model training across varied chemical inputs and CVF outcomes.",{"name":82,"@type":73,"acceptedAnswer":83},"Which alloying elements are identified as significantly influencing CVF determination?",{"text":84,"@type":76},"The ML results indicate that carbon (C), chromium (Cr), and molybdenum (Mo) have significant influence on CVF determination.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]