| Download | - View final version: Evaluation of key spatiotemporal learners for print track anomaly classification using melt pool image streams (PDF, 842 KiB)
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| DOI | Resolve DOI: https://doi.org/10.1016/j.ifacol.2023.10.1235 |
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| Author | Search for: Cherif, Lynn; Search for: Safdar, Mutahar; Search for: Lamouche, Guy1; Search for: Wanjara, Priti2; Search for: Paul, Padma; Search for: Wood, Gentry; Search for: Zimmermann, Max; Search for: Hannesen, Florian; Search for: Zhao, Yaoyao Fiona |
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| Affiliation | - National Research Council of Canada. Digital Technologies
- National Research Council of Canada. Aerospace
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| Format | Text, Article |
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| Conference | 22nd IFAC World Congress, July 9-14, 2023, Yokohama, Japan |
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| Subject | additive manufacturing; melt pool monitoring; defect classification; spatiotemporal learning |
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| Abstract | Recent applications of machine learning in metal additive manufacturing (MAM) have shown great potential to resolve critical barriers to MAM's widespread adoption. Recent research on the topic highlights the significance of using melt pool signatures to predict defects on-the-fly. While high-fidelity melt pool image data has the potential to enable accurate predictions, hardly any work exists on the use of state-of-the-art spatiotemporal models to leverage the information embedded in the transient and sequential nature of the additive process. This work introduces and implements some of the major deep spatiotemporal learners that can be adapted to classify melt pool image streams from different materials, systems, and applications. In this regard, two-stream networks with a spatial and temporal stream, a recurrent spatial network, and a factorized 3D convolutional neural network were investigated herein. The generalization abilities of these models to perturbations in the melt pool image data are tested using data perturbation techniques that are grounded in palpable process situations. The implemented architectures exhibit the ability to learn the spatiotemporal features of the melt pool image sequences. However, only the Kinetics400 pre-trained SlowFast network, which belongs to the two-stream category, showed strong generalisation abilities to data perturbations. |
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| Publication date | 2023-11-22 |
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| Publisher | Elsevier
International Federation of Automatic Control (IFAC) |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 51907788-df4f-411c-a589-dc91f25a4219 |
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| Record created | 2023-11-23 |
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| Record modified | 2023-11-24 |
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