Review of image segmentation techniques for layup defect detection in the Automated Fiber Placement process: A comprehensive study to improve AFP inspection

Sebastian Meister*, Mahdieu A.M. Wermes, Jan Stüve, Roger M. Groves

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

2 Citations (Scopus)
16 Downloads (Pure)

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 %. 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.

Original languageEnglish
Pages (from-to)2099-2119
Number of pages21
JournalJournal of Intelligent Manufacturing
Volume32
Issue number8
DOIs
Publication statusPublished - 2021

Keywords

  • Adaptive thresholding
  • Automated fiber placement
  • Computer vision
  • Image segmentation
  • Inline inspection
  • Laser line scan sensor

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