[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125333-en":3,"doc-seo-125333-105":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},125333,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Classification of defects in additively manufactured nickel alloys using supervised machine learning","Additively manufactured components can contain undesirable microstructural features such as cracks, pores, and lack of fusion defects, creating major challenges for engineers when parts are used in structure-critical applications. Manual metallographic workflows require defect identification, counting, and size-distribution measurement, making process development slow and labor-intensive. This study evaluates two supervised machine learning approaches—k-nearest neighbours and decision trees—for automatically classifying typical defects observed during metallographic examination of additively manufactured nickel alloys.","[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk](https://eprints.whiterose.ac.uk)  \nUniversities of Leeds, Sheffield and York  \nDeposited via The University of Sheffield.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/id/eprint/231983/](https://eprints.whiterose.ac.uk/id/eprint/231983/)  \nVersion: Published Version  \nArticle:  \nAziz, U. , Bradshaw, A. , Lim, J. et al. (2023) Classification of defects in additively manufactured nickel alloys using supervised machine learning. Materials Science and Technology, 39 (16) . pp. 2464-2468. ISSN: 0267-0836  \n[https://doi.org/10.1080/02670836.2023.2207337](https://doi.org/10.1080/02670836.2023.2207337)  \nReuse  \nThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) licence. This licence only allows you to download this work and share it with others as long as you credit the authors, but you can’t change the article in any way or use it commercially. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)  \nTakedown  \nIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by  \nemailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request.  \nMATERIALS SCIENCE AND TECHNOLOGY 2023, VOL. 39, NO. 16, 2464–2468  \n[https://doi.org/10.1080/02670836.2023.2207337](https://doi.org/10.1080/02670836.2023.2207337)  \nClassification of defects in additively manufactured nickel alloys using supervised machine learning  \nUbaid Aziz, Andrew Bradshaw, Justin Lim and Meurig Thomas   \nInterdisciplinary Programmes in Engineering, University of Sheffield, Sheffield, UK  \nABSTRACT  \nThe presence of undesirable microstructural features in additively manufactured components, such as cracks, pores and lack of fusion defects presents a challenge for engineers, particularly if these components are applied in structure-critical applications. Such features might need to be manually classified, counted and their size distributions measured during metallographic evaluation, which is a time-consuming task. In this study, the performance of two supervised machine learning methods (kth-nearest neighbours and decision trees) to automatically classify typical defects found during metallographic examination of additively manufactured nickel alloys is briefly outlined and discussed.  \nARTICLE HISTORY  \nReceived 25 January 2023 Revised 17 April 2023 Accepted 20 April 2023  \nKEYWORDS  \nAdditive manufacturing; nickel alloys; defects; machine learning  \nAdditive manufacturing is a near-net shape production technology that utilises a high-energy heat source to selectively melt or fuse together metallic powder to produce a three-dimensional part [1] . Notwithstanding the potential benefits of additive manufacturing technologies for near-net shape production, the presence of internal and external defects in additively manufactured components presents a problem for engineers, particularly if these components are applied in structure-critical applications and in situations where a component is subject to a fluctuating load [2,3] . Common types of internal defects found within additively manufactured parts include; lack of fusion defects, gas porosity, solidification cracks, impurities, solid-state cracks and void formation due to key-hole collapse in certain high energy density processes [4] . Due to their size, such defects (anomalous features or undesirable microstructural features) are detected using X-ray computational tomography [5] or, perhaps more commonly, during metallographic inspection using light microscopy. These features might need to be manually classified, counted and their size distributions measured, particularly in the early stages of process development where parameter","cbCaiuvehxQb0FCG","https://ap.wps.com/l/cbCaiuvehxQb0FCG","pdf",706843,1,6,"English","en",105,"# Abstract\n# Introduction and Motivation\n## Defect types in additive manufacturing\n## Need for automated classification\n# Machine Learning Background\n## Supervised learning concepts\n## Prior work in materials applications","[{\"question\":\"Why are defects in additively manufactured components a concern?\",\"answer\":\"Defects such as cracks, pores, and lack of fusion can undermine engineering performance, especially in structure-critical applications and under fluctuating loads.\"},{\"question\":\"What manual tasks does metallographic evaluation require?\",\"answer\":\"Engineers often need to manually classify and count defects and measure their size distributions during light microscopy-based metallographic inspection.\"},{\"question\":\"Which supervised machine learning methods are evaluated for defect classification?\",\"answer\":\"The study examines k-nearest neighbours and decision trees to automatically classify typical defects in additively manufactured nickel alloys.\"}]","Classification of defects in additively manufactured nickel alloys using supervised machine learning | PDF",1785898234,15,{"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},"classification-of-defects-in-additively-manufactured-nickel-alloys-using-supervised-machine-learning","",{"@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/classification-of-defects-in-additively-manufactured-nickel-alloys-using-supervised-machine-learning/125333/",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},"Why are defects in additively manufactured components a concern?","Question",{"text":75,"@type":76},"Defects such as cracks, pores, and lack of fusion can undermine engineering performance, especially in structure-critical applications and under fluctuating loads.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What manual tasks does metallographic evaluation require?",{"text":80,"@type":76},"Engineers often need to manually classify and count defects and measure their size distributions during light microscopy-based metallographic inspection.",{"name":82,"@type":73,"acceptedAnswer":83},"Which supervised machine learning methods are evaluated for defect classification?",{"text":84,"@type":76},"The study examines k-nearest neighbours and decision trees to automatically classify typical defects in additively manufactured nickel alloys.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"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"]