[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123645-en":3,"doc-seo-123645-105":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},123645,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Towards an accurate estimation of heat flux distribution in metal cutting by machine learning","This study develops a machine learning-based inverse identification method to estimate the heat flux distribution on the rake face of cutting tools during machining. Temperature data are obtained from thermocouples embedded in the tool and combined with heat-transfer finite element simulations to generate training data for the ML model. The predicted heat flux distribution is validated by comparing it against results from finite element machining simulations, demonstrating clear potential for more efficient heat-flux estimation.","Towards an accurate estimation of heat flux distribution in metal cutting by machine learning  \nDownloaded from: [https://research.chalmers.se](https://research.chalmers.se), 2023-09-08 04:52 UTC  \nCitation for the original published paper (version of record):  \nErtürk, A., Malakizadi, A., Larsson, R. (2023) . Towards an accurate estimation of heat flux distribution in metal cutting by machine learning. Procedia CIRP, 117: 359-364.  \n[http://dx.doi.org/10.1016/j.procir.2023.03.061](http://dx.doi.org/10.1016/j.procir.2023.03.061)  \nN. B. When citing this work, cite the original published paper.  \nresearch.chalmers.se offers the possibility of retrieving research publications produced at Chalmers University of Technology. It covers all kind of research output: articles, dissertations, conference papers, reports etc. since 2004.  \nresearch.chalmers.se is administrated and maintained by Chalmers Library  \n(article starts on next page)  \n[Available online at www.sciencedirect.com](Available online at www.sciencedirect.com)  \nScienceDirect  \nProcedia CIRP 117 (2023) 359–364  \n19th CIRP Conference on Modeling of Machining Operations  \nTowards an accurate estimation of heat flux distribution in metal cutting by  \nmachine learning  \nAhmet Semih Erturk*, a , Amir Malakizadib , Ragnar Larssonaa Division of Materials and Computational Mechanics, Department of Industrial and Materials Science,  \nChalmers University of Technology, G¨oteborg SE-41296, Sweden  \nb Division of Materials and Manufacture, Department of Industrial and Materials Science,  \nChalmers University of Technology, G¨oteborg SE-41296, Sweden  \n* Corresponding author. E-mail address: [erturk@chalmers.se](erturk@chalmers.se)  \nAbstract  \nThis study presents a machine learning-based approach for inverse identification of heat flux distribution on the rake face of the cutting tools in machining. This approach includes temperature measurements from thermocouples embedded in the tool and heat transfer finite element (FE) simulations to create the data required to train the ML model. The identified heat flux distribution is compared with the distribution from FE machining simulations for validation. The results show a clear potential to estimate the heat flux distribution in machining more efficiently by using an ML-based inverse approach.  \n© 2023 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer review under the responsibility of the scientific committee of the 19th CIRP Conference on Modeling of Machining Operations Keywords: Heat flux; Heat transfer simulation; Inverse identification; Machine Learning; Machining; Metal cutting; Temperature  \n1. Introduction  \nHeat generation plays a significant role in machining processes due to its effects on s urface i ntegrity a nd t ool wear. The difficulties in performing temperature measurements during machining have encouraged researchers to pursue analytical or numerical methods for the estimation of temperature atthe tool-chip interface. An alternative approach is to determine the heat flux using inverse methodologies, where the experimental temperature measurements are used as a reference to identify the heat flux on the contact area. For this, the finite element method (FEM) or the finite difference method (FDM) is generally used to solve the heat transfer problem and the results are compared with the experimental results, generally measured by embedded thermocouples within the tools or workpiece material. As an example, Yvonnet et al. [1] presented an inverse approach for temperature identification. The authors performed a machining experiment and measured the temperature with a thermocouple during the operation. By using these measurements and creating a finite element (FE) model, the heat flux distribution on the rake face in 2D and the heat transfer co-  \nefficient ","cbCaihf14wzGQUI5","https://ap.wps.com/l/cbCaihf14wzGQUI5","pdf",2212378,1,7,"English","en",105,"# Introduction\n## Inverse heat flux identification\n## Prior work and related inverse approaches\n## Motivation for more accurate estimation","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To estimate the heat flux distribution on the rake face of cutting tools using a machine learning-based inverse approach.\"},{\"question\":\"How is training data for the ML model generated?\",\"answer\":\"By combining thermocouple temperature measurements with heat transfer finite element (FE) simulations to create the dataset used to train the model.\"},{\"question\":\"How are the results validated?\",\"answer\":\"The identified heat flux distribution is compared with the heat flux distribution obtained from FE machining simulations to assess accuracy.\"}]","Towards an accurate estimation of heat flux distribution in metal cutting by machine learning | PDF",1785817809,18,{"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},"towards-an-accurate-estimation-of-heat-flux-distribution-in-metal-cutting-by-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/towards-an-accurate-estimation-of-heat-flux-distribution-in-metal-cutting-by-machine-learning/123645/",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","2026-08-04",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 is the main goal of the study?","Question",{"text":75,"@type":76},"To estimate the heat flux distribution on the rake face of cutting tools using a machine learning-based inverse approach.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is training data for the ML model generated?",{"text":80,"@type":76},"By combining thermocouple temperature measurements with heat transfer finite element (FE) simulations to create the dataset used to train the model.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the results validated?",{"text":84,"@type":76},"The identified heat flux distribution is compared with the heat flux distribution obtained from FE machining simulations to assess accuracy.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"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"]