[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127090-en":3,"doc-seo-127090-105":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},127090,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Machine learning methods to predict the fatigue life of selectively laser melted Ti6Al4V components","The paper aims to predict the fatigue life of Selectively Laser Melted (SLMed) Ti6Al4V components using machine learning models driven by process parameters, thermal treatments, surface treatments, and stress amplitude. The goal is to reduce the cost and time of additional fatigue testing while enabling more reliable predictive fatigue design. Models are trained on experimental datasets from the literature and validated on separate testing sets to evaluate extrapolation limits and compare methods. Parameter-sensitivity mapping identifies how individual SLM settings affect life.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nMachine learning methods to predict the fatigue life of selectively laser melted Ti6Al4V components  \nOriginal  \nMachine learning methods to predict the fatigue life of selectively laser melted Ti6Al4V components / Centola, Alessio; Ciampaglia, Alberto; Tridello, Andrea; Paolino, Davide Salvatore. -In: FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES. -ISSN 8756-758X. -46:11(2023), pp. 4350-4370. [10 . 1111/ffe. 14125]  \nAvailability:  \nThis version is available at: 11583/2989416 since: 2024-06-11T09:48:10Z  \nPublisher: WILEY  \nPublished  \nDOI:10.1111/ffe.14125  \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)  \n17 February 2025  \nReceived: 17 June 2023 Revised: 24 July 2023 Accepted: 28 July 2023  \nDOI: 10.1111/ffe.14125  \nSPECIAL I SSUE A RTICL E  \nMachine learning methods to predict the fatigue life of selectively laser melted Ti6Al4V components  \nAlessio Centola  | Alberto Ciampaglia | Andrea Tridello  | Davide Salvatore Paolino   \nDepartment of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy  \nCorrespondence  \nAlessio Centola, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, 10129 Italy. Email: alessio.centola@polito.it  \n[Correction added on October 5, 2023, after first online publication: Article category corrected from “Original Article”to “Special Issue Article”.]  \nAbstract  \nThe aim of the present paper is to predict the fatigue life of Selectively Laser Melted (SLMed) Ti6Al4V components via the process parameters, the thermal treatments, the surface treatments and the stress amplitude, adopting machine learning techniques to reduce the cost of further fatigue testing, and to deliver better predictive fatigue designs. The studies resulted in reliable algorithms capable of predicting trustful fatigue curves. The methods have been trained with experimental data available in the literature and validated on testing sets to assess the extrapolation limits and to compare the different methods. The behavior of the networks has also been mapped by varying one SLM process parameter at the time, highlighting how each one affects the life.  \nKEYWOR DS  \nadditive manufacturing, fatigue, machine learning, neural networks, physics informed, selective laser melting, Ti6Al4V  \nHighlights  \nMachine learning can be used to reduce time and costs for fatigue characterization.  \nMachine learning predicts the life of SLMed Ti6Al4V specimens effectively. The physics informed neural network performs best.  \n1 | INTRODUCTION  \nIt is widely known that Titanium alloys are difficult to work with traditional manufacturing processes, like the machining process. Indeed, titanium alloys are much harder to shape with traditional methods because they are not capable of conducting heat efficiently.1 This  \nmeans that the cutting tool will absorb nearly all the heat that is generated during manufacturing, causing it to be degraded sooner.1 Titanium is also tough, and a high shear force is needed to create a chip. These reasons make machining titanium tedious and expensive.1 Moreover, Titanium alloys are used in biomedical applications,2 whose parts and implants are of complicated  \nThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.  \n© 2023 The Authors. Fatigue & Fracture of Engineering Materials & Structures published by John Wiley & Sons Ltd.  \nCENTOLA ET AL.  \n4351  \nand often personalized shapes. Additive Manufacturing (AM) processes, on the other hand, allow to overcome these issues. Basically, it is based on adding material layer by layer, where","cbCaintbelL2EyXl","https://ap.wps.com/l/cbCaintbelL2EyXl","pdf",3413504,1,22,"English","en",105,"# Highlights\n## Machine learning for fatigue characterization\n## Best-performing physics-informed approach\n# Introduction\n## Challenges in machining titanium alloys\n## Advantages of additive manufacturing for complex Ti6Al4V parts\n## Fatigue limitations of SLM Ti6Al4V and key influencing factors","[{\"question\":\"What inputs are used to predict the fatigue life of SLMed Ti6Al4V components?\",\"answer\":\"The models use SLM process parameters, thermal treatments, surface treatments, and stress amplitude to predict fatigue life.\"},{\"question\":\"How are the machine learning methods evaluated in the study?\",\"answer\":\"They are trained using experimental data reported in the literature and validated on testing sets to assess extrapolation limits and compare prediction performance across methods.\"},{\"question\":\"Which approach performs best according to the article’s highlights?\",\"answer\":\"The physics-informed neural network is reported as the best-performing method for predicting the life of SLMed Ti6Al4V specimens.\"}]","Machine learning methods to predict the fatigue life of selectively laser melted Ti6Al4V components | 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inputs are used to predict the fatigue life of SLMed Ti6Al4V components?","Question",{"text":75,"@type":76},"The models use SLM process parameters, thermal treatments, surface treatments, and stress amplitude to predict fatigue life.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the machine learning methods evaluated in the study?",{"text":80,"@type":76},"They are trained using experimental data reported in the literature and validated on testing sets to assess extrapolation limits and compare prediction performance across methods.",{"name":82,"@type":73,"acceptedAnswer":83},"Which approach performs best according to the article’s highlights?",{"text":84,"@type":76},"The physics-informed neural network is reported as the best-performing method for predicting the life of SLMed Ti6Al4V 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