A Deep Learning Approach for Defect Detection and Segmentation in X-Ray Computed Tomography Slices of Additively Manufactured Components Article Swipe
Pradip Acharya
,
Tsuchin Philip Chu
,
Khaled Ragab
,
Subash Kharel
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5121/ijaia.2022.13401
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5121/ijaia.2022.13401
Additive manufacturing is an emerging and crucial technology that can overcome the limitations of traditional manufacturing techniques to accurately manufacture highly complex parts. X-ray Computed Tomography (XCT) is a widely used method for non-destructive testing of AM parts. However, detection and segmentation of defects in XCT images of AM have many challenges due to contrast, size, and appearance of defects. This study developed deep learning techniques for detecting and segmenting defects in XCT images of AM. Due to a large number of required defect annotations, this paper applied image processing techniques to automate the defect labeling process. A single-stage object detection algorithm (YOLOv5) was applied to the problem of defect detection in image data. Three different variants of YOLOv5 were implemented and their performances were compared. U-Net was applied for defect segmentation in XCT slices. Finally, this research demonstrates that deep learning techniques can improve the automatic defect detection and segmentation in XCT data of AM.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5121/ijaia.2022.13401
- https://doi.org/10.5121/ijaia.2022.13401
- OA Status
- bronze
- Cited By
- 7
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4297794710
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4297794710Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5121/ijaia.2022.13401Digital Object Identifier
- Title
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A Deep Learning Approach for Defect Detection and Segmentation in X-Ray Computed Tomography Slices of Additively Manufactured ComponentsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-07-31Full publication date if available
- Authors
-
Pradip Acharya, Tsuchin Philip Chu, Khaled Ragab, Subash KharelList of authors in order
- Landing page
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https://doi.org/10.5121/ijaia.2022.13401Publisher landing page
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https://doi.org/10.5121/ijaia.2022.13401Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://doi.org/10.5121/ijaia.2022.13401Direct OA link when available
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Segmentation, Artificial intelligence, Computer science, Deep learning, Process (computing), Image segmentation, Pattern recognition (psychology), Computer vision, Computed tomography, Object detection, Operating system, Radiology, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 3Per-year citation counts (last 5 years)
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27Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.segmenting | 69 |
| abstract_inverted_index.techniques | 16, 65, 90, 142 |
| abstract_inverted_index.technology | 7 |
| abstract_inverted_index.implemented | 120 |
| abstract_inverted_index.limitations | 12 |
| abstract_inverted_index.manufacture | 19 |
| abstract_inverted_index.traditional | 14 |
| abstract_inverted_index.annotations, | 84 |
| abstract_inverted_index.demonstrates | 138 |
| abstract_inverted_index.performances | 123 |
| abstract_inverted_index.segmentation | 41, 131, 150 |
| abstract_inverted_index.single-stage | 98 |
| abstract_inverted_index.manufacturing | 1, 15 |
| abstract_inverted_index.non-destructive | 33 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.62553646 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |