Automated vision system for magnetic particle inspection of crankshafts using convolutional neural networks


This paper proposes a fully automated vision system to inspect the whole surface of crankshafts, based on the magnetic particle testing technique. A stepper motor combined with multiple cameras is needed to ensure the inspection of the whole surface of the crankshaft in real-time. Due to the very textured surface of crankshafts and the variability in defect shapes and types, defect detection methods based on deep learning algorithms, more precisely convolutional neural networks (CNNs), become a more efficient solution than traditional methods. This paper discusses the various approaches of defect detection with CNNs, mainly classification, object detection, and semantic segmentation. The advantages and weaknesses of each approach for real-time defect detection are presented. It is important to note that the proposed visual inspection system only replaces the manual inspection of crankshafts conducted by operators at the end of the magnetic particle testing procedure, allowing for an easy integration in any crankshaft factory.

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This work was supported by the TRAC project, grant FUI 24 by the Unique Inter-ministerial Fund.

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Correspondence to Karim Tout.

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Tout, K., Meguenani, A., Urban, JP. et al. Automated vision system for magnetic particle inspection of crankshafts using convolutional neural networks. Int J Adv Manuf Technol 112, 3307–3326 (2021).

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  • Magnetic particle inspection
  • Defect detection
  • Vision system
  • Quality control
  • Convolutional neural networks