Automatic Classification of XCT Images in Manufacturing
Autor: | Florian Reiterer, Bertram Sabrowsky-Hirsch, Roxana-Maria Holom, Christian Gusenbauer, Ricardo Fernández Gutiérrez, Michael Reiter, Josef Scharinger |
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Přispěvatelé: | RISC Software GmbH, Nemak Linz GmbH, Johannes Kepler University Linz [Linz] (JKU), Ilias Maglogiannis, John Macintyre, Lazaros Iliadis, TC 12, WG 12.5 |
Rok vydání: | 2021 |
Předmět: |
X-ray computed tomography
0209 industrial biotechnology business.industry Computer science Deep learning Reliability (computer networking) Process (computing) Quality control Pattern recognition 02 engineering and technology Convolutional neural network Class (biology) Manufacturing 020901 industrial engineering & automation Operator (computer programming) Nondestructive testing Machine learning 0202 electrical engineering electronic engineering information engineering [INFO]Computer Science [cs] 020201 artificial intelligence & image processing Support system Artificial intelligence business Casting |
Zdroj: | IFIP Advances in Information and Communication Technology ISBN: 9783030791490 AIAI IFIP Advances in Information and Communication Technology 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI) 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.220-231, ⟨10.1007/978-3-030-79150-6_18⟩ |
Popis: | Part 7: Convolutional NN; International audience; X-ray computed tomography (XCT) is an established non-destructive testing (NDT) method that, in combination with automatic evaluation routines, can be successfully used to establish a reliable 100% inline inspection system for defect detection of cast parts. While these systems are robust in automatically localizing suspected defects, human know-how in a secondary assessment and decision-making step remains indispensable to avoid an excess of rejected parts. Rather than changing the existing defect detection system and risking difficult to anticipate changes to a solid evaluation process, we propose the integration of human know-how in a subsequent support system through end-to-end learning. Using XCT data and the corresponding decisions performed by the XCT operator, we aim to support and possibly automate the secondary quality assessment process. In our paper we present a Convolutional Neural Network (CNN) architecture to predict both, the final decision of the XCT operator and a defect class indication, for cast parts rejected by the defect detection system based on XCT slice images. On a dataset of 19,459 defect records categorized in 7 classes, we achieved an accuracy of 92% for the decision and 93% for the defect class indication on the testing split. We further show that, by binding decisions to the reliability of the predicted defect class, our model has the potential to enhance also a production process with a near-faultless condition. Based on production-line data, we estimate that our model can reliably relabel 11% of defects reported during production and provide a defect class indication for another 57%. |
Databáze: | OpenAIRE |
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