Explainability of deep learning classifier decisions for optical detection of manufacturing defects in the automated fiber placement process

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

doi:10.1117/12.2592584

20 June 2021 Explainability of deep learning classifier decisions for optical detection of manufacturing defects in the automated fiber placement process

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

Author Affiliations +

Proceedings Volume 11787, Automated Visual Inspection and Machine Vision IV; 1178705 (2021) https://doi.org/10.1117/12.2592584
Event: SPIE Optical Metrology, 2021, Online Only

Abstract

Automated fibre layup techniques are commonly used composite manufacturing processes in the aviation sector and require a manual visual inspection. Neural Network classification of defects has the potential to automate this visual inspection, however, the machine decision-making processes are hard to verify. Thus, we present an approach for visualising Convolutional Neural Network (CNN) based classifications of manufacturing defects and quantifying its robustness suitably. Our investigations have shown that especially Smoothed Integrated Gradients and DeepSHAP are particularly well suited for the visualisation of CNN classifications. The Smoothed Integrated Gradients technique also reveals advantages in robustness when evaluating degraded input images.

Conference Presentation

Citation Download Citation

Sebastian Meister, Mahdieu A. M. Wermes, Jan Stüve, and Roger M. Groves "Explainability of deep learning classifier decisions for optical detection of manufacturing defects in the automated fiber placement process", Proc. SPIE 11787, Automated Visual Inspection and Machine Vision IV, 1178705 (20 June 2021); https://doi.org/10.1117/12.2592584

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