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Investigations on Explainable Artificial Intelligence methods for the deep learning classification of fibre layup defect in the automated composite manufacturing

Meister, Sebastian and Wermes, Mahdieu A. M. and Stüve, Jan and Groves, Roger M. (2021) Investigations on Explainable Artificial Intelligence methods for the deep learning classification of fibre layup defect in the automated composite manufacturing. Composites Part B Engineering. Elsevier. doi: 10.1016/j.compositesb.2021.109160. ISSN 1359-8368.

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Abstract

Automated fibre layup techniques are widely used in the aviation sector for the efficient production of composite components. However, the required manual inspection can take up to 50 % of the manufacturing time. The automated classification of fibre layup defects with Neural Networks potentially increases the inspection efficiency. However, the machine decision-making processes of such classifiers are difficult to verify. Hence, we present an approach for analysing the classification procedure of fibre layup defects. Therefore, we comprehensively evaluate 20 Explainable Artificial Intelligence methods from the literature. Accordingly, the techniques Smoothed Integrated Gradients, Guided Gradient Class Activation Mapping and DeepSHAP are applied to a Convolutional Neural Network classifier. These methods analyse the neural activations and robustness of a classifier for an unknown and manipulated input data. Our investigations show that especially Smoothed Integrated Gradients and DeepSHAP are well suited for the visualisation of such classifications. Additionally, maximum-sensitivity and infidelity calculations confirm this behaviour. In future, customers and developers could apply the presented methods for the certification of their inspection systems.

Item URL in elib:https://elib.dlr.de/143297/
Document Type:Article
Title:Investigations on Explainable Artificial Intelligence methods for the deep learning classification of fibre layup defect in the automated composite manufacturing
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Meister, SebastianSebastian.Meister (at) dlr.dehttps://orcid.org/0000-0002-8193-1143
Wermes, Mahdieu A. M.Mahdieu.Wermes (at) dlr.deUNSPECIFIED
Stüve, JanJ.Stueve (at) tudelft.nlhttps://orcid.org/0000-0003-1483-2476
Groves, Roger M.R.M.Groves (at) tudelft.nlhttps://orcid.org/0000-0001-9169-9256
Date:22 July 2021
Journal or Publication Title:Composites Part B Engineering
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1016/j.compositesb.2021.109160
Publisher:Elsevier
ISSN:1359-8368
Status:Published
Keywords:Defects Non-destructive testing Process monitoring Automation
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Components and Systems
DLR - Research area:Aeronautics
DLR - Program:L CS - Components and Systems
DLR - Research theme (Project):L - Production Technologies
Location: Stade
Institutes and Institutions:Institute of Composite Structures and Adaptive Systems > Composite Process Technology
Deposited By: Meister, Sebastian
Deposited On:09 Aug 2021 10:48
Last Modified:16 Sep 2021 09:56

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