[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128702-en":3,"doc-seo-128702-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128702,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Characterisation and prediction of mechanical properties in laser powder bed fusion-printed parts - a comparative analysis using machine learning","This study investigates how process parameters—scanning strategy, build orientation, and hatching distance—affect the mechanical properties of AlSi10Mg parts produced by Laser Powder Bed Fusion (L-PBF). Parameters are varied within defined ranges and analysed statistically to quantify impacts on tensile strength and ductility, showing scanning strategy as most influential, then hatching distance, while build orientation drives anisotropy. Microstructural evidence links process conditions to mechanical strength mechanisms. Machine learning models (RFR, SVR, ANN) predict tensile strength and ductility, with RFR and SVR outperforming ANN on limited datasets.","Materials Technology  \nAdvanced Performance Materials  \nISSN: (Print) (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/ymte20)[www.tandfonline.com/journals/ymte20](homepage: www.tandfonline.com/journals/ymte20)  \nCharacterisation and prediction of mechanical properties in laser powder bed fusion-printed parts: a comparative analysis using machine learning  \nNaol Dessalegn Dejene & Hirpa G. Lemu  \nTo cite this article: Naol Dessalegn Dejene & Hi rpa G. Lemu (2024) Characterisation and prediction of mechanical properties in laser powder bed fusion-printed parts: a comparative analysis using machine learning, Materials Technology, 39: 1, 2419228, DOI:  \n10. 1080/10667857 .2024.2419228  \nTo link to this article: [https://doi.org/10.1080/10667857.2024.2419228](https://doi.org/10.1080/10667857.2024.2419228)  \n© 2024 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group.  \n\n|  Published online: 25 Oct 2024. |  |\n| --- | --- |\n|  | Submit your article to this journal  |\n|  | Article views: 215 |\n|  | View related articles  |\n|  View Crossmark data |  |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=ymte20](https://www.tandfonline.com/action/journalInformation?journalCode=ymte20)  \nMATERIALS TECHNOLOGY  \n2024, VOL. 39, NO. 1, 2419228 [https://doi.org/10.1080/10667857.2024.2419228](https://doi.org/10.1080/10667857.2024.2419228)  \nCharacterisation and prediction of mechanical properties in laser powder bed fusion-printed parts: a comparative analysis using machine learning  \nNaol Dessalegn Dejene a,b and Hirpa G. Lemu b  \na Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, Stavanger, Norway; bDepartment of Mechanical Engineering, College of Engineering & Technology, Wallaga University, Nekemte, Ethiopia  \nABSTRACT  \nThis study investigates the effects of process parameters including scanning strategy, build orientation, and hatching distance on the mechanical properties of AlSi10Mg parts produced by Laser Powder Bed Fusion (L-PBF) . The experiment varied these parameters within defined ranges and used statistical analysis to evaluate their impact on tensile strength and ductility. Results showed that scanning strategy had the greatest influence, followed by hatching distance, while build orientation affected anisotropic properties. Microstructural analysis showed clear correlation between process conditions and mechanical strength, thereby showing the underlying mechanisms that govern material behavior. Moreover, Machine learning models, including Random Forest Regression (RFR), Support Vector Regression (SVR), and Artificial Neural Networks (ANNs), were applied to predict tensile strength and ductility characteristics. RFR and SVR outperformed ANNs, showing high predictive accuracy with limited datasets. These findings emphasize the importance of optimizing L-PBF process parameters to minimize anisotropy and achieve consistent mechanical properties in produced parts.  \nARTICLE HISTORY  \nReceived 24 April 2024 Accepted 15 October 2024  \nKEYWORDS  \nAdditive manufacturing; laser powder bed fusion; mechanical property; scanning strategy; machine learning; AlSi10Mg  \nIntroduction  \nAdditive manufacturing (AM), as defined by the ISO/ ASTM terminology standard, encompasses various techniques for joining materials to produce parts using data from the 3D models [ 1] . According to ISO/ASTM 52,900:2015, these methods are classified into seven basic groups. Each category offers unique qualities and applications, covering vat polymerisation, powder bed fusion, material jetting, material extrusion, directed energy deposition and binder jetting [2] . This classification provides a solid basis for understanding the diverse landscape of additive manufacturing technology.  \nSignificant amount of statistical data demonstrates the expanding importance of AM in the manufacturing in","cbCaivm2MRD5hMQ7","https://ap.wps.com/l/cbCaivm2MRD5hMQ7","pdf",9847750,3,1,15,"English","en",105,"# Abstract\n## Process parameters and their effects\n## Statistical and microstructural analysis\n## Machine learning models for prediction","[{\"question\":\"Which L-PBF process parameters are examined in this study?\",\"answer\":\"The study evaluates scanning strategy, build orientation, and hatching distance as the key process parameters affecting mechanical behavior of AlSi10Mg parts.\"},{\"question\":\"How do scanning strategy, hatching distance, and build orientation influence mechanical properties?\",\"answer\":\"Results indicate scanning strategy has the greatest influence, followed by hatching distance, while build orientation governs anisotropic mechanical properties.\"},{\"question\":\"Which machine learning models are used to predict tensile strength and ductility, and how do they compare?\",\"answer\":\"Random Forest Regression (RFR), Support Vector Regression (SVR), and Artificial Neural Networks (ANN) are applied. RFR and SVR outperform ANNs and achieve high predictive accuracy using limited datasets.\"}]","Characterisation and prediction of mechanical properties in laser powder bed fusion-printed parts - a comparative analysis using machine learning | PDF",1786002758,38,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"characterisation-and-prediction-of-mechanical-properties-in-laser-powder-bed-fusion-printed-parts-a-comparative-analysis-using-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/characterisation-and-prediction-of-mechanical-properties-in-laser-powder-bed-fusion-printed-parts-a-comparative-analysis-using-machine-learning/128702/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which L-PBF process parameters are examined in this study?","Question",{"text":76,"@type":77},"The study evaluates scanning strategy, build orientation, and hatching distance as the key process parameters affecting mechanical behavior of AlSi10Mg parts.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do scanning strategy, hatching distance, and build orientation influence mechanical properties?",{"text":81,"@type":77},"Results indicate scanning strategy has the greatest influence, followed by hatching distance, while build orientation governs anisotropic mechanical properties.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning models are used to predict tensile strength and ductility, and how do they compare?",{"text":85,"@type":77},"Random Forest Regression (RFR), Support Vector Regression (SVR), and Artificial Neural Networks (ANN) are applied. 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