Print Email Facebook Twitter Explainability of deep learning classifier decisions for optical detection of manufacturing defects in the Automated Fiber Placement process Title Explainability of deep learning classifier decisions for optical detection of manufacturing defects in the Automated Fiber Placement process Author Meister, S. (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)) Wermes, Mahdieu A.M. (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)) Stuve, Jan (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)) Groves, R.M. (TU Delft Structural Integrity & Composites) Contributor Beyerer, Jurgen (editor) Heizmann, Michael (editor) Date 2021 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. 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. Subject CNNComposite ManufacturingComputer VisionDefect classificationsInline InspectionLaser Line Scan SensorXAI To reference this document use: http://resolver.tudelft.nl/uuid:e866e5dc-a024-4f22-b927-8ac31e06a9ca DOI https://doi.org/10.1117/12.2592584 Publisher SPIE Embargo date 2022-01-15 ISBN 9781510644083 Source Automated Visual Inspection and Machine Vision IV, 11787 Event Automated Visual Inspection and Machine Vision IV 2021, 2020-06-21 → 2020-06-25, Virtual, Online, Germany Series Proceedings of SPIE - The International Society for Optical Engineering, 0277-786X, 11787 Bibliographical note Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type conference paper Rights © 2021 S. Meister, Mahdieu A.M. Wermes, Jan Stuve, R.M. Groves Files PDF 1178705.pdf 6.92 MB Close viewer /islandora/object/uuid:e866e5dc-a024-4f22-b927-8ac31e06a9ca/datastream/OBJ/view