Abstract
Remanufacturing of automotive components involves restoring cores from end-of-life vehicles (ELV) to a condition where the component can be used again. Implementing image processing technique in the visual inspection stage is used to reduce cost and time while increasing accuracy. This research aims to perform real-time surface visual defect detection that is simpler and less costly which commonly uses machine learning and/or deep learning techniques. The methods used in this research are a combination of image processing and computer vision techniques to great effect. The techniques used are a pixel-based contour detection and image subtraction. For this research, connecting will be the sample component to test the algorithm. This approach will take less time to train since it compares the non-defective connecting rod image with the defective connecting rod. The defects that are tested in this research are cracks and buckles, which commonly occur in connecting rod failure due to high load and stress. There are different settings of threshold and illumination that were tested, such as different thresholds of 50, 60, and 70, also illumination colour temperatures such as white, natural, and warm. After 270 trials with different settings and defects, the proposed algorithm achieved a 93% accuracy using a 70 per cent threshold and white light (3000–3500 K). The findings suggest that the implementation of a pixel-based contour detection through image subtraction holds promise as a cost-effective alternative to utilizing machine learning and/or deep learning techniques.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Schlüter M et al (2021) AI-enhanced identification, inspection and sorting for reverse logistics in remanufacturing. Procedia CIRP 98:300–305. https://doi.org/10.1016/j.procir.2021.01.107
Nowakowski P (2013) Reuse of automotive components from dismantled end of life vehicles. Transp. Probl. 8(4):17–25
Zhang H, Li T, Liu Z, Jiang Q (2013) Handbook of manufacturing engineering and technology. Handb Manuf Eng Technol Nov 2015, 2013. https://doi.org/10.1007/978-1-4471-4976-7
Wahab DA, Amelia L, Hooi NK, Haron CHC, Azhari CH (2015) The application of artificial intelligence in pptimisation of automotive components for reuse. J Achiev Mater Manuf Eng 31(2):595–601
Nwankpa C, Eze S, Ijomah W, Gachagan A, Marshall S (2021) Achieving remanufacturing inspection using deep learning. J Remanufacturing 11(2):89–105. https://doi.org/10.1007/s13243-020-00093-9
Aravapalli SRM (2022) An automatic inspection approach for remanufacturing components using object detection
Cha Y, Choi W (2017) Deep learning-based crack damage detection using convolutional neural networks 32:361–378. https://doi.org/10.1111/mice.12263
Gong W, Zhang K, Yang C, Yi M, Wu J (2020) Adaptive visual inspection method for transparent label defect detection of curved glass bottle. In: Proceeding 2020 international conferences computer vision, image deep learn. CVIDL 2020, May, pp 90–95. https://doi.org/10.1109/CVIDL51233.2020.00024
Witek L (2019) Stress and failure analysis of the connecting rod of diesel engine. 97(December 2018):374–382. https://doi.org/10.1016/j.engfailanal.2019.01.004
Culjak I, Abram D, Pribanic T, Dzapo H, Cifrek M (2012) A brief introduction to OpenCV. In: MIPRO 2012—35th international convention on information and communication technology, electronics and microelectronics—Proceeding, pp 1725–1730
Yang D, Peng B, Al-huda Z, Malik A, Zhai D (2022) An overview of edge and object contour detection. Neurocomputing 488:470–493. https://doi.org/10.1016/j.neucom.2022.02.079
Stavropoulos P, Papacharalampopoulos A, Athanasopoulou L, Kampouris K, Lagios P (2022) Designing a digitalised cell for remanufacturing of automotive frames. Procedia CIRP 109:513–519. https://doi.org/10.1016/j.procir.2022.05.287
Gedraite ES, Hadad M (2011) Investigation on the effect of a Gaussian blur in image filtering and segmentation. Proceding Elmar—International Symposium Electron Mar, September, pp 393–396
Taşan H (2019) A hybrid method for object tracking in video atilim. Αγαη 8(5):55
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Abdalla, I., Sakundarini, N., May May, C.C., Chandesa, T. (2024). Detection of Fault Features in Remanufacturing of Automotive Components Using Image Processing and Computer Vision Techniques. In: Mohd. Isa, W.H., Khairuddin, I.M., Mohd. Razman, M.A., Saruchi, S.'., Teh, SH., Liu, P. (eds) Intelligent Manufacturing and Mechatronics. iM3F 2023. Lecture Notes in Networks and Systems, vol 850. Springer, Singapore. https://doi.org/10.1007/978-981-99-8819-8_12
Download citation
DOI: https://doi.org/10.1007/978-981-99-8819-8_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8818-1
Online ISBN: 978-981-99-8819-8
eBook Packages: EngineeringEngineering (R0)