Deploying Machine Learning for Radiography of Aerospace Welds
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2024/01/28
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Indian UGC (journal)
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Refrences54
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Topias Tyystjärvi
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Peter Fridolf
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Anders Rosell
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Iikka Virkkunen
Abstract
Cite
Tyystjärvi, Topias, et al. “Deploying Machine Learning for Radiography of Aerospace Welds”. Journal of Nondestructive Evaluation, vol. 43, no. 1, 2024, https://doi.org/10.1007/s10921-023-01041-w.
Tyystjärvi, T., Fridolf, P., Rosell, A., & Virkkunen, I. (2024). Deploying Machine Learning for Radiography of Aerospace Welds. Journal of Nondestructive Evaluation, 43(1). https://doi.org/10.1007/s10921-023-01041-w
Tyystjärvi T, Fridolf P, Rosell A, Virkkunen I. Deploying Machine Learning for Radiography of Aerospace Welds. Journal of Nondestructive Evaluation. 2024;43(1).
Journal Category
Technology
Electrical engineering
Electronics
Nuclear engineering
Materials of engineering and construction
Mechanics of materials
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Approach to weld segmentation and defect classification in radiographic images of pipe welds | NDT & E International |
| 16 | 2022 |
Automated defect detection in digital radiography of aerospace welds using deep learning | Welding in the World |
| 18 | 2022 |
| An automatic welding defect location algorithm based on deep learning | NDT & E International |
| 53 | 2021 |
| Convolution neural network model with improved pooling strategy and feature selection for weld defect recognition | Welding in the World |
| 22 | 2021 |
| Automated Defect Recognition on X-ray Radiographs of Solid Propellant Using Deep Learning Based on Convolutional Neural Networks | Journal of Nondestructive Evaluation |
| 24 | 2021 |