Algorithm assessment for layup defect segmentation from laser line scan sensor based image data

Sebastian Meister; Mahdieu Amin Mahdieu Wermes; Jan Stüve; Roger M. Groves

doi:10.1117/12.2558434

23 April 2020 Algorithm assessment for layup defect segmentation from laser line scan sensor based image data

Sebastian Meister, Mahdieu Amin Mahdieu Wermes, Jan Stüve, Roger M. Groves

Proceedings Volume 11379, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2020; 1137918 (2020) https://doi.org/10.1117/12.2558434
Event: SPIE Smart Structures + Nondestructive Evaluation, 2020, Online Only

Abstract

The Automated Fiber Placement process is established in the aerospace industry for the production of composite components. This technique places several narrow material strips in parallel. Within current industrial Automated Fiber Placement processes the visual inspection takes typically up to 50% of overall production time. Furthermore, inspection quality highly depends on the inspector. Therefore, automation of visual inspection offers a great improvement potential. To ensure reliable defect detection the segmentation of individual defects must be investigated. For this reason, this paper focusses on an assessment of defect segmentation algorithms. Therefore, 29 structural, statistical and spectral algorithms from related work were assessed, theoretically, using the 12 most relevant criteria as assessed from literature and process requirements. Then, seven most auspicious algorithms were analysed in detail. For reasons of determinism, Neural Network approaches are not part of this paper. Manually labelled prepreg defect images from a laser line scan sensor were used for tests. The test samples contain five defect types with 50 samples of each. Additionally, layups without defects were analysed. It was concluded that Adaptive Thresholding works best for global defect segmentation. The Cell Wise Standard Deviation Thresholding performs also quite well, but is very sensitive to grid size. Feasible algorithms perform reliable defect segmentation for layed up material.

Conference Presentation

Citation Download Citation

Sebastian Meister, Mahdieu Amin Mahdieu Wermes, Jan Stüve, and Roger M. Groves "Algorithm assessment for layup defect segmentation from laser line scan sensor based image data", Proc. SPIE 11379, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2020, 1137918 (23 April 2020); https://doi.org/10.1117/12.2558434

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