[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120972-en":3,"doc-seo-120972-105":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},120972,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Towards Early Damage Detection during the Disassembly of Threaded Fasteners using Machine Learning - Abstract","Aircraft engines and modular components undergo scheduled maintenance, repair, and overhaul (MRO) inspections, often requiring complete or partial disassembly. Bolted joints experience strong environmental degradation that increases manual disassembly effort and the risk of damage. When workers use ratchets or impact wrenches, excessive loosening torque can tear screw heads, leaving the shaft in the base thread. The proposed strategy monitors loosening torque and rotation angle, applying machine learning to detect and predict breakage and classify damage categories, enabling adapted disassembly to avoid complex rework. Preliminary tests confirm feasibility for aircraft disassembly.","[Available online at www.sciencedirect.com](Available online at www.sciencedirect.com)  \nScienceDirect  \nProcedia CIRP 116 (2023) 480–485  \n30th CIRP Life Cycle Engineering Conference  \nTowards Early Damage Detection during the Disassembly of Threaded  \nFasteners using Machine Learning  \nRichard Bl¨umel*a,, Annika Raatza  \na Leibniz University Hannover, Institute of Assembly Technology, An der Universit¨at 2, 30823 Garbsen, Germany  \n* Corresponding author. Tel.: +49-511-762-18248 ; fax: +[49-511-762-18251.](49-511-762-18251. E-mail address: bluemel@match.uni-hannover.de)[ E-mail address:](49-511-762-18251. E-mail address: bluemel@match.uni-hannover.de)[ bluemel@match.uni-hannover.de](49-511-762-18251. E-mail address: bluemel@match.uni-hannover.de)  \nAbstract  \nAt regular intervals, aircraft and their components, such as engines, are inspected following specified maintenance, repair and overhaul (MRO) guidelines. The modular design of the engines supports complete or partial disassembly for inspection of all relevant parts. The often used bolted joints are significantly altered by the harsh environmental conditions, severely increasing the disassembly effort usually carried out manually. Using tools like a ratchet or rotary impact wrenches, workers apply torque on the screw head to loosen the screw. If the loosening torque exceeds the material limits, screw heads are torn off, leaving the shaft in the base thread. This article presents a strategy to prevent disassembly damages through precise monitoring of the loosening torques and angle of rotation. Based on this data, a machine learning algorithm detects and predicts potential breakage, to allow adapted disassembly strategies to prevent complex rework. The algorithm will classify potential damage into different categories. Preliminary testing proved the applicability of machine learning toward aircraft disassembly.  \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)) Peer-review under responsibility of the scientific committee of the 30th CIRP Life Cycle Engineering Conference  \nKeywords: Disassembly; Threaded Fasteners; Damage Detection using Machine Learning;  \n1. Introduction  \nAircraft engines are maintained, repaired and overhauled (MRO) in regular intervals to ensure safe operation. Alongside an inspection, they are disassembled to a certain stage depending on the specific maintenance task. Thus, components or assemblies that need to be inspected, repaired or replaced are accessed. To ensure that maintenance operations are carried out efficiently, modern turbofan engines have a modular design and are assembled by bolted joints. Likewise, system components such as cooling, electrical power generation, fuel components, hydraulic or pneumatic systems essential for the operation of the engine are connected to the engine with threaded fastenings. Due to the harsh operating conditions of the engines, the assemblies, components and their fasteners change their properties, necessitating maintenance. On detailed examination of the threaded connections, the mentioned changes in properties lead to the joints altering. Consequently, the needed loosening torque is increased, and the fasteners can only be loosened with substantial effort. That, in turn, increases the risk of damaging the threaded fasteners during disassembly, for example, by tearing off the screw heads. As a result, the damaged fasten-  \ners have to be removed, leading to a significant additional effort due to potential further disassembly operations, extending maintenance intervals, and eventually financial loss, according to a maintenance service provider.  \nTo prevent such damages and thus minimize the risk of further consequences, in the transfer project ”Strategies for piezoassisted disassembly of bolted joints” of the Collaborative Research Center (","cbCaip0MbUz9Vndl","https://ap.wps.com/l/cbCaip0MbUz9Vndl","pdf",3255145,1,6,"English","en",105,"# Introduction\n## Related Work\n# Procedure and Methods\n# Results\n# Discussion\n# Future Research","[{\"question\":\"Why does disassembly damage risk increase during aircraft maintenance?\",\"answer\":\"Harsh operating conditions alter the properties of threaded fasteners, raising the required loosening torque and increasing the chance of tearing off screw heads during manual loosening.\"},{\"question\":\"What signals does the machine learning approach use to detect potential breakage?\",\"answer\":\"The strategy precisely monitors loosening torque and the angle of rotation during the unscrewing process.\"},{\"question\":\"How does early damage detection support gentler disassembly?\",\"answer\":\"By forewarning potential tear-off, the system enables adapted disassembly strategies that prevent destructive outcomes and reduce the need for costly postprocessing and rework.\"}]","Towards Early Damage Detection during the Disassembly of Threaded Fasteners using Machine Learning - 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