A Fault Detection Robotic Cell Application Based on Deep Learning and Image Processing Hybrid Approach for Quality Control of Automotive Parts
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Int Publ Ag
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In 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.
Açıklama
Anahtar Kelimeler
Deep learning, Image processing, Robotic quality control, YOLOv5
Kaynak
Iranian Journal of Science and Technology-Transactions of Electrical Engineering
WoS Q Değeri
Q4
Scopus Q Değeri
Q2
Cilt
49
Sayı
1
