A Fault Detection Robotic Cell Application Based on Deep Learning and Image Processing Hybrid Approach for Quality Control of Automotive Parts

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dc.authorid0000-0001-6888-5755
dc.contributor.authorKir, Hilal
dc.contributor.authorAdar, Nurettin Gokhan
dc.contributor.authorYazar, Mustafa
dc.date.accessioned2026-02-08T15:15:06Z
dc.date.available2026-02-08T15:15:06Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn this study, the development of a robotic cell that combines deep learning and image processing hybrid approach has been addressed in order to increase the accuracy and efficiency of the quality control of automotive parts. In the automotive industry, manual quality control processes performed by operators are susceptible to errors and inaccuracies, leading to the passage of faulty parts and subsequent inefficiencies, wasted time, and increased costs. To overcome these challenges, this study introduces a fault detection robotic cell that combines deep learning and image processing techniques for quality control of automotive parts at Sahinkul Machine Spare Parts Manuf. Ltd. Co.. The robotic cell uses image processing to inspect geometric tolerances, including hole diameter, part geometry and the presence of holes. However, the complex geometry of bolt threads requires the use of the YOLOv5 deep learning algorithm to assess their quality. A dataset consisting of 3500 bolt thread images was collected for training and validation, with 2800 images used for training, 350 for validation, and the remaining 350 for testing purposes. The experimental results show that the fault detection robotic workcell achieves an approximate success rate of 97.4% in inspecting the quality of the selected parts. By combining deep learning and image processing, this study provides a reliable solution to improve the accuracy and efficiency of quality control processes in the automotive industry.
dc.description.sponsorshipTUBITAK BIDEB (Turkish Scientific and Technological Research Council, Scientist Support Department) [119C053]; Sahinkul Machine Spare Parts Manufacturing Ltd. Co, Research and Development Center [Ar-Ge 2020 017]
dc.description.sponsorshipScholarship in this work was supported by TUBITAK BIDEB (Turkish Scientific and Technological Research Council, Scientist Support Department) (Project No: 119C053). This work was financially supported by Sahinkul Machine Spare Parts Manufacturing Ltd. Co, Research and Development Center with the project number of Ar-Ge 2020 017.
dc.identifier.doi10.1007/s40998-024-00768-0
dc.identifier.endpage485
dc.identifier.issn2228-6179
dc.identifier.issn2364-1827
dc.identifier.issue1
dc.identifier.scopus2-s2.0-105001069864
dc.identifier.scopusqualityQ2
dc.identifier.startpage471
dc.identifier.urihttps://doi.org/10.1007/s40998-024-00768-0
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5597
dc.identifier.volume49
dc.identifier.wosWOS:001356454900001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Int Publ Ag
dc.relation.ispartofIranian Journal of Science and Technology-Transactions of Electrical Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectDeep learning
dc.subjectImage processing
dc.subjectRobotic quality control
dc.subjectYOLOv5
dc.titleA Fault Detection Robotic Cell Application Based on Deep Learning and Image Processing Hybrid Approach for Quality Control of Automotive Parts
dc.typeArticle

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