[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121465-en":3,"doc-seo-121465-105":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":20,"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},121465,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Machine Learning in Industrial Remanufacturing - A Five-Year Review of Applications","Remanufacturing advances circular economy and sustainable manufacturing by extending product lifecycles and reducing resource use, yet it remains labor-intensive and often economically difficult. This structured literature review examines recent machine learning applications in industrial remanufacturing from 2020 to 2025, emphasizing inspection and disassembly stages where ML appears most frequently. Results show convolutional neural networks for automated visual inspection and defect detection, and reinforcement learning for disassembly planning and control, especially in human-robot collaborative systems, improving productivity and cost efficiency while noting limited real-industry validation.","Hugo Huotari  \nMACHINE LEARNING IN  \nINDUSTRIAL REMANUFACTURING A Five-Year Review of Applications  \nBachelor’s Thesis  \nFaculty of Management and Business Examiner: Jaakko Siltaloppi May 2025  \ni  \nABSTRACT  \nHugo Huotari: Machine Learning In Industrial Remanufacturing: A Five-Year Review Of Applications: A Five-Year Review of Applications  \nBachelor’s Thesis  \nTampere University  \nBachelor’s Programme in Business and Technology, Industrial Engineering and Management May 2025  \nRemanufacturing is one of the cornerstones in advancing circular economy objectives and sustainable manufacturing operations by extending product lifecycles and minimizing resource use. However, the process is labour-intensive and often economically unviable. This structured literature review explores the recent applications of machine learning in the industrial remanufacturing process, with a focus on inspection and disassembly stages where machine learning has been employed the most in journal articles published between 2020 and 2025.  \nThe findings reveal that convolutional neural networks are extensively used for automating visual inspections, improving defect detection precision, and reducing throughput times. An array of reinforcement learning techniques shows great promise in optimizing disassembly planning and control tasks, particularly in human-robot collaborative systems. These technologies collectively enhance productivity, reduce dependence on manual labour, and improve the overall costefficiency of the industrial remanufacturing process. While the findings outline considerable promise, the lack of real-industry cases limits practical validation.  \nThe originality of this thesis has been verified using the Turnitin Originality Check service.  \nii  \nTIIVISTELMÄ  \nHugo Huotari: Koneoppiminen teollisessa uudelleenvalmistuksessa: Viiden vuoden katsaussovelluksiin  \nKandidaatintutkielma  \nTampereen yliopisto  \nTeknis-taloudellinen kandidaattiohjelma, tuotantotalous  \nToukokuu 2025  \nUudelleenvalmistus on yksi keskeisimmistä kiertotalouden ja kestävän teollisen tuotannon implementaatiostrategioista, sillä se pidentää tuotteiden elinkaarta ja vähentää tuotannossa tarvittavien luonnonvarojen käyttöä . Nykyisellään uudelleenvalmistusprosessit vaativat kuitenkin runsaasti manuaalista työtä ja ovat usein taloudellisesti kovin haastava toteuttaa.  \nTässä systemaattisessa kirjallisuuskatsauksessa tarkastellaan koneoppimisen sovelluksia teollisen uudelleenvalmistuksen kontekstissa, keskittyen erityisesti eri tarkastus-ja purkuvaiheisiin. Analyysi kattaa vuosina 2020–2025 julkaistut tieteelliset artikkelit, joissa koneoppimista on sovellettu näihin prosessin osa-alueisiin.  \nTulosten perusteella konvoluutioneuroverkkoja hyödynnetään laajasti visuaalisen tarkastuksen automatisoinnissa, parantaen vikojen tunnistamisen tarkkuutta ja lyhentäen prosessin läpimenoaikoja merkittävästi. Lisäksi vahvistusoppimisen menetelmiä hyödynnetään purkusuunnittelun ja-ohjauksen optimoinnissa, erityisesti erilaisissa ihmisen ja robotin yhteistoimintaan perustuvissa järjestelmissä .  \nNäiden teknologioiden käyttö yhdessä lisää prosessin tuottavuutta, vähentää manuaalisentyön tarvetta prosessissaja parantaa sen kustannustehokkuutta. Vaikka tulokset ovatkin lupaaviauudelleenvalmistuksen kehittämisen näkökulmasta, todellisiin teollisiin sovelluksiin perustuvanempiirisen tutkimuksen puute rajoittaa johtopäätösten yleistettävyyttä ja sovellettavuutta käytännön tasolla.  \nTämän julkaisun alkuperäisyys on tarkastettu Turnitin Originality Check-ohjelmalla.  \niii  \nUSE OF AI IN THESIS  \nI have utilised AI tools in my thesis:  \n☐ No  \n☒ Yes  \nThe AI tools utilised in my thesis and their purposes are described below:  \nChatGPT (GPT-4 Turbo)  \n- Generating ideas, especially for structuring the thesis  \n- Language enhancements and editing  \n- Clarifying complex concepts in simpler terms  \n- Proofreading and reviewing content Scopus AI  \n- Assisting with idea generation  \n- Familiarizing ","cbCaisqcVItgcz70","https://ap.wps.com/l/cbCaisqcVItgcz70","pdf",570312,1,30,"English","en",105,"# INTRODUCTION\n# REMANUFACTURING: CONCEPT AND INDUSTRIAL PROCESS\n## Understanding Remanufacturing\n## Industrial Process of Remanufacturing\n# MACHINE LEARNING: PRINCIPLES AND METHODS\n## Fundamentals of Machine Learning\n## Overview of Machine Learning Methods Used in This Thesis\n## Convolutional Neural Networks and Transfer Learning\n## Reinforcement Learning\n## Performance Metrics for Evaluating ML Models in This Thesis\n# APPLICATIONS OF ML IN REMANUFACTURING PROCESSES\n## ML Applications: Inspection\n## ML Applications: Disassembly\n# CONCLUSIONS\n# REFERENCES","[{\"question\":\"What stages of industrial remanufacturing does the review focus on?\",\"answer\":\"The review concentrates on inspection and disassembly stages, identifying them as areas where machine learning has been used most in the reviewed journal articles.\"},{\"question\":\"Which machine learning methods are most commonly reported for inspection?\",\"answer\":\"Convolutional neural networks are widely applied to automate visual inspection, enhance defect detection accuracy, and reduce processing throughput time.\"},{\"question\":\"How does reinforcement learning contribute to disassembly in these applications?\",\"answer\":\"Reinforcement learning methods are used to optimize disassembly planning and control tasks, particularly in human-robot collaborative systems.\"}]","Machine Learning in Industrial Remanufacturing - 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