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Review of image segmentation techniques for layup defect detection in the Automated Fiber Placement process

Author

Listed:
  • Sebastian Meister

    (German Aerospace Center (DLR))

  • Mahdieu A. M. Wermes

    (German Aerospace Center (DLR))

  • Jan Stüve

    (German Aerospace Center (DLR))

  • Roger M. Groves

    (Delft University of Technology)

Abstract

The aerospace industry has established the Automated Fiber Placement process as a common technique for manufacturing fibre reinforced components. In this process multiple composite tows are placed simultaneously onto a tool. Currently in such processes manual testing requires often up to 50% of the manufacturing duration. Moreover, the accuracy of quality assurance varies significantly with the inspector in charge. Thus, inspection automation provides an effective way to increase efficiency. However, to achieve a proper inspection performance, the segmentation of layup defects need to be examined. In order to improve such defect detection systems, this paper performs a comprehensive ranking of segmentation techniques. Thus, 29 statistical, spectral and structural algorithms from related work were evaluated based on nine substantial criteria as assessed from literature and process requirements. For reasons of determinism and easy technology transferability without the need of much training data, the development of new Machine Learning algorithms is not part of this paper. Afterwards, seven of the most auspicious algorithms were studied experimentally. Therefore, laser line scan sensor depth maps from fibre placement defects were utilised. Furthermore noisy images were generated and applied for testing algorithm robustness. The test data contained five defect categories with 50 samples per class. It was concluded that Adaptive Thresholding and Cell Wise Standard Deviation Thresholding work best yielding detection accuracies mostly $$> 97$$ > 97 %. Noteworthy is that influenced input data can affect the detection results. Feasible algorithms with sensible parameter settings were able to perform reliable defect segmentation for layed material.

Suggested Citation

  • Sebastian Meister & Mahdieu A. M. Wermes & Jan Stüve & Roger M. Groves, 2021. "Review of image segmentation techniques for layup defect detection in the Automated Fiber Placement process," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2099-2119, December.
  • Handle: RePEc:spr:joinma:v:32:y:2021:i:8:d:10.1007_s10845-021-01774-3
    DOI: 10.1007/s10845-021-01774-3
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    References listed on IDEAS

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    1. Sebastian Meister & Nantwin Möller & Jan Stüve & Roger M. Groves, 2021. "Synthetic image data augmentation for fibre layup inspection processes: Techniques to enhance the data set," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1767-1789, August.
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    Cited by:

    1. Swarit Anand Singh & K. A. Desai, 2023. "Automated surface defect detection framework using machine vision and convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1995-2011, April.
    2. Zichen Bai & Junfeng Jing, 2024. "Mobile-Deeplab: a lightweight pixel segmentation-based method for fabric defect detection," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3315-3330, October.

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