[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122293-en":3,"doc-seo-122293-105":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},122293,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Identification Of Investigation Procedures To Predict Work Roll Fatigue For Developing Machine Learning Applications - A Systematic Literature Review","Machine learning enables effective optimization of machines and production systems, especially in hot rolling where failures can be difficult to explain. In radial-axial ring rolling, mandrel failure is unpredictable, lacks a directly identifiable cause, and may occur multiple times per week, leading to broken rolls, downtime, defective rings, and potential damage to other parts. With abundant ring-rolling production data available, a systematic literature review (SLR) focuses on work-roll wear in hot rolling. Results support selecting features and investigation procedures for predicting mandrel failure via machine-learning models.","CONFERENCE ON PRODUCTION SYSTEMS AND LOGISTICS  \nCPSL 2023-2  \n5th Conference on Production Systems and Logistics  \nIdentification Of Investigation Procedures To Predict Work Roll Fatigue For Developing Machine Learning Applications – A Systematic Literature Review  \nTobias Moser 1*, Johannes Seitz 1, Enes Alp 1, Bernd Kuhlenkötter 1  \n1Chair of Production Systems / Ruhr-Universität Bochum, Bochum, Germany  \nAbstract  \nMachine learning approaches present significant opportunities for optimizing existing machines and production systems. Particularly in hot rolling processes, great potential for optimization can be exploited. Radial-axial ring rolling is a crucial process utilized to manufacture seamless rings. However, the failure of the mandrel represents a defect within the ring rolling process that currently cannot be adequately explained. Mandrel failure is unpredictable, occurs without a directly identifiable reason, and can appear several times a week depending on the ring rolling mill and capacity utilization. Broken rolls lead to unscheduled production downtimes, defective rings and can damage other machine parts. Considering the extensive recording of production data in ring rolling, the implementation of machine learning models for the prediction of such roll breaks offers great potential. To present a comprehensive overview of the potential influencing factors which are possibly relevant to the lifetime of mandrels, a systematic literature review (SLR) focusing on work roll wear in hot rolling processes is conducted. Based on the results of the SLR, a first selection of features and the used investigation procedures are presented. The insights can be used for the prediction of mandrel failure with machine learning models in further work.  \nKeywords  \nRadial-Axial Ring Rolling; Work Rolls; Fatigue; Systematic Literature Review; Machine Learning;  \n1. Introduction  \nSeamlessly formed, ring-shaped components with high required specifications, such as highly dynamic load capacity and high product variability, are necessary for many machines and systems in all branches of industry. Typical areas of application are rail transport, aerospace, the automotive industry, plant and energy plant engineering, and special machine construction. Radial-axial ring rolling (RARR) (see Figure 1) is an important process for the production of such components. [1] A currently not sufficiently explainable failure in the domain of RARR is the failure of the mandrel, which occurs unpredictably and without a directly identifiable reason. The large amount of influencing factors (e.g. rolling temperature, rolling pressure, rolling material)[2], which also have non-linear interdependencies, hinders the use of proven research methods to identify qualitative and quantitative influences that are related to failure. [3] In this context, machine learning algorithms offer a new approach for identifying and weighting the influencing factors and predicting the remaining lifetime of the mandrel. It has already been demonstrated by Fahle et al. that machine learning models are suitable for applications in the field of RARR. [4] Furthermore, comprehensive data recording is  \nDOI: [https://doi.org/10.15488/15](https://doi.org/10.15488/15)􀀖00  \nISSN: 2701-6277  \n268  \nalready available in many industrial companies, which is a basis requirement for the implementation of machine learning applications. [5]  \na)  \nradial-axial ring rolling  \nb)  \nmandrel  \nmandrel guide roll ring  \nupper axial roll  \nforming roll guide roll lower axial roll  \nFigure 1 : a) Scheme of the RARR process; b) illustration of the mandrel  \nTo exploit the potential of machine learning algorithms effectively, it is beneficial to gain a fundamental domain knowledge about the conventional investigation procedures. However, given the limited availability of publications pertaining to the field of RARR, which were also included in this systematic literature review, it is not feasible to estab","cbCaikGA2p4ZhKBy","https://ap.wps.com/l/cbCaikGA2p4ZhKBy","pdf",1430856,1,14,"English","en",105,"# 1. Introduction\n# 2. Structured Review Methodology\n## Literature search strategy\n## Database and filters","[{\"question\":\"What problem does the paper address in radial-axial ring rolling?\",\"answer\":\"The paper targets the failure of the mandrel, which occurs unpredictably and without a directly identifiable reason, causing downtime and defective rings.\"},{\"question\":\"Why is machine learning considered for predicting mandrel failure?\",\"answer\":\"Machine learning can identify and weight many influencing factors with non-linear interdependencies and support prediction of remaining mandrel lifetime.\"},{\"question\":\"What does the systematic literature review focus on?\",\"answer\":\"It focuses on work roll wear in hot rolling processes, classifying research procedures and assessing their suitability for predicting work roll life.\"}]","Identification Of Investigation Procedures To Predict Work Roll Fatigue For Developing Machine Learning Applications - A Systematic Literature Review | 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problem does the paper address in radial-axial ring rolling?","Question",{"text":75,"@type":76},"The paper targets the failure of the mandrel, which occurs unpredictably and without a directly identifiable reason, causing downtime and defective rings.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is machine learning considered for predicting mandrel failure?",{"text":80,"@type":76},"Machine learning can identify and weight many influencing factors with non-linear interdependencies and support prediction of remaining mandrel lifetime.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the systematic literature review focus on?",{"text":84,"@type":76},"It focuses on work roll wear in hot rolling processes, classifying research procedures and assessing their suitability for predicting work roll 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