[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121357-en":3,"doc-seo-121357-105":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},121357,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Probabilistic Machine Learning for preventing fatigue failures in Additively Manufactured SS316L","This study presents a probabilistic machine learning approach to predict and improve the fatigue performance of additively manufactured SS316L components. By analyzing key manufacturing parameters as process settings, thermal treatments and surface treatments, the developed models provide statistical estimations of fatigue strength to support extension of fatigue life. Trained on an experimental database of fatigue tests, a Bayesian Neural Network separates model uncertainty from uncertainty inherent in the fatigue phenomenon. The method robustly predicts Probabilistic StressLife (PSN) curves and shows increased robustness and trustworthiness versus deterministic ML, helping postpone fatigue failures in critical applications.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nProbabilistic Machine Learning for preventing fatigue failures in Additively Manufactured SS316L  \nOriginal  \nProbabilistic Machine Learning for preventing fatigue failures in Additively Manufactured SS316L / Centola, Alessio; Ciampaglia, Alberto; Paolino, Davide Salvatore; Tridello, Andrea. -In: ENGINEERING FAILURE ANALYSIS. -ISSN 1350-6307. -ELETTRONICO. -168:(2025), pp. 1-23. [10 . 1016/j.engfailanal.2024. 109081]  \nAvailability:  \nThis version is available at: 11583/2994912 since: 2025-06-10T04:11:25Z  \nPublisher: Elsevier  \nPublished  \nDOI:10.1016/j.engfailanal.2024.109081  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n21 February 2026  \nEngineering Failure Analysis 168 (2025) 109081  \nContents lists available at ScienceDirect  \nEngineering Failure Analysis  \njournal [homepage: www.elsevier.com/locate/engfailanal](homepage: www.elsevier.com/locate/engfailanal)  \n| Probabilistic Machine Learning for preventing fatigue failures in\u003Cbr>Additively Manufactured SS316L\u003Cbr>Alessio Centola *, Alberto Ciampaglia , Davide Salvatore Paolino , Andrea Tridello Politecnico di Torino, Department of Mechanical and Aerospace Engineering, Torino 10129, Italy |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Fatigue\u003Cbr>Probabilistic machine learning Machine learning\u003Cbr>Process parameters\u003Cbr>Design\u003Cbr>Additive manufacturing SS316L\u003Cbr>Failure prevention |  | This study presents a probabilistic machine learning approach to predict and improve the fatigue performance of additively manufactured SS316L components. By analyzing key manufacturing parameters as process settings, thermal treatments and surface treatments, the developed models provide statistical estimations of fatigue strength, that provides valuable insights to extend fatigue life. Trained on an experimental database of fatigue tests, the Bayesian Neural Network (BNN) is employed to separate model uncertainty, related to data limitations, from the uncertainty inherent in the fatigue phenomenon. This approach robustly predicts Probabilistic StressLife (PSN) curves, offering valuable insights into the impact of manufacturing parameters on fatigue resistance, allowing to further postpone fatigue failures. The results demonstrate increased robustness and trustworthiness compared to other deterministic machine learning models, making this method suitable for critical applications where failure prevention is crucial. |  |\n\n1. Introduction  \nAdditive manufacturing (AM) is an emerging production process redefining the landscape of modern engineering, with Powder Bed Fusion – Laser Beam (PBF-LB) being a paramount technology in producing lightweight metal components with optimized geometries.  \nAbbreviations: AM, Additive Manufacturing; BNN, Bayesian Neural Network; C90, Confidence level at 90%; CDF, Cumulative distribution function; Ed, Volumetric energy density; f, failure; FFNN, Feed-Forward Neural Network; FFNN-L, Feed-Forward Neural Network Likelihood; h, Hatch Distance [μm]; HCF, High Cycle Fatigue; HIP, Hot Isostatic Pressing Thermal Treatment; LOF, Lack of fusion; LPBF, Laser power bed fusion; m, R50 curve slope coefficient; ML, Machine Learning; MLLE, Maximum Log-Likelihood Estimate; MLLEf, Maximum log-likelihood estimate of a failure; MLLEr, Maximum log-likelihood estimate of a runout; MSE, Mean squared error; Nf, Number of cycles to failure; NLL, negative log-likelihood; NN, Neural Network; Nr, Number of cycles ofa runout specimen; P, Laser Power [W]; PBF-LB, Powder Bed Fusion – Laser Beam; PDF, Probability density function; PINN, Physics Informed Neural Network; PP, Process Parameters; PSN, Probabilistic Stress-Life (Curves); q, R50 intercept coefficient; qR90C90, R90C90 intercept coefficient; r, runout; R90, Reliability","cbCaijGFwqGLVpfi","https://ap.wps.com/l/cbCaijGFwqGLVpfi","pdf",983884,1,24,"English","en",105,"# Introduction\n## Additive manufacturing context and material focus\n# Methodology and probabilistic modeling\n## Bayesian Neural Network and uncertainty separation\n# Results and predictive performance\n## PSN curve prediction and comparisons\n# Conclusions and implications for failure prevention","[{\"question\":\"What problem does the probabilistic machine learning approach address?\",\"answer\":\"It predicts and improves fatigue performance of additively manufactured SS316L components so fatigue life can be extended and fatigue failures can be postponed.\"},{\"question\":\"Which manufacturing factors are used to model fatigue strength?\",\"answer\":\"The models analyze key manufacturing parameters, including process settings, thermal treatments, and surface treatments.\"},{\"question\":\"How does the Bayesian Neural Network contribute to the predictions?\",\"answer\":\"It separates model uncertainty due to data limitations from uncertainty inherent in the fatigue phenomenon, enabling robust Probabilistic StressLife (PSN) curve prediction.\"}]","Probabilistic Machine Learning for preventing fatigue failures in Additively Manufactured SS316L | PDF",1785735226,60,{"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},"probabilistic-machine-learning-for-preventing-fatigue-failures-in-additively-manufactured-ss316l","",{"@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/probabilistic-machine-learning-for-preventing-fatigue-failures-in-additively-manufactured-ss316l/121357/",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","2026-08-03",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 probabilistic machine learning approach address?","Question",{"text":75,"@type":76},"It predicts and improves fatigue performance of additively manufactured SS316L components so fatigue life can be extended and fatigue failures can be postponed.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which manufacturing factors are used to model fatigue strength?",{"text":80,"@type":76},"The models analyze key manufacturing parameters, including process settings, thermal treatments, and surface treatments.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the Bayesian Neural Network contribute to the predictions?",{"text":84,"@type":76},"It separates model uncertainty due to data limitations from uncertainty inherent in the fatigue phenomenon, enabling robust Probabilistic StressLife (PSN) curve prediction.","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},[93,97,101,105,109,114,119,122,127,130,134],{"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":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"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"]