Automatic defect detection in radiographic images of casting components: a comprehensive review.
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| Title: | Automatic defect detection in radiographic images of casting components: a comprehensive review. |
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| Authors: | Kumar, S.1 kumar.s.cqnde@sathyabama.ac.in, Rani, B. Sheela2 |
| Source: | Insight: Non-Destructive Testing & Condition Monitoring. Jun2026, Vol. 68 Issue 6, p389-396. 8p. |
| Subjects: | Radiographic processing, Metal castings, Image processing, Deep learning, Machine learning, Nondestructive testing, Outlier detection, Industrial applications |
| Abstract: | Casting is a critical manufacturing process widely employed in industries such as nuclear, automotive, petrochemical and aerospace. Ensuring the structural integrity and reliability of casting products is paramount, as undetected defects may lead to catastrophic failures in service. Radiographic testing (RT) is one of the most extensively applied non-destructive testing (NDT) techniques for casting inspection, due to its ability to reveal internal flaws such as porosity, shrinkage cavities, cracks and inclusions. However, manual interpretation of radiographic images requires a lot of time, is subjective and is prone to human error, especially in complex castings with overlapping defect patterns. With recent advances in digital image processing, machine learning (ML) and deep learning (DL), automatic defect detection in radiographic images has emerged as a powerful alternative to traditional film interpretation. This review provides a comprehensive overview of state-of-the-art research in the field of automatic defect recognition for casting products. It examines classical machine learning approaches and modern deep learning frameworks, highlighting their respective advantages, limitations and industrial applications. Benchmark datasets, international standards and performance evaluation metrics are also presented to evaluate the effectiveness of automated defect detection systems. Finally, challenges such as limited labelled datasets, noise artefacts and regulatory acceptance are discussed and future research directions are proposed. This review consolidates current advances while identifying gaps and opportunities for future developments in automatic defect detection during radiographic testing of casting products. [ABSTRACT FROM AUTHOR] |
| Copyright of Insight: Non-Destructive Testing & Condition Monitoring is the property of British Institute of Non-Destructive Testing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194538791 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Automatic defect detection in radiographic images of casting components: a comprehensive review. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kumar%2C+S%2E%22">Kumar, S.</searchLink><relatesTo>1</relatesTo><i> kumar.s.cqnde@sathyabama.ac.in</i><br /><searchLink fieldCode="AR" term="%22Rani%2C+B%2E+Sheela%22">Rani, B. Sheela</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Insight%3A+Non-Destructive+Testing+%26+Condition+Monitoring%22">Insight: Non-Destructive Testing & Condition Monitoring</searchLink>. Jun2026, Vol. 68 Issue 6, p389-396. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Radiographic+processing%22">Radiographic processing</searchLink><br /><searchLink fieldCode="DE" term="%22Metal+castings%22">Metal castings</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Nondestructive+testing%22">Nondestructive testing</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+applications%22">Industrial applications</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Casting is a critical manufacturing process widely employed in industries such as nuclear, automotive, petrochemical and aerospace. Ensuring the structural integrity and reliability of casting products is paramount, as undetected defects may lead to catastrophic failures in service. Radiographic testing (RT) is one of the most extensively applied non-destructive testing (NDT) techniques for casting inspection, due to its ability to reveal internal flaws such as porosity, shrinkage cavities, cracks and inclusions. However, manual interpretation of radiographic images requires a lot of time, is subjective and is prone to human error, especially in complex castings with overlapping defect patterns. With recent advances in digital image processing, machine learning (ML) and deep learning (DL), automatic defect detection in radiographic images has emerged as a powerful alternative to traditional film interpretation. This review provides a comprehensive overview of state-of-the-art research in the field of automatic defect recognition for casting products. It examines classical machine learning approaches and modern deep learning frameworks, highlighting their respective advantages, limitations and industrial applications. Benchmark datasets, international standards and performance evaluation metrics are also presented to evaluate the effectiveness of automated defect detection systems. Finally, challenges such as limited labelled datasets, noise artefacts and regulatory acceptance are discussed and future research directions are proposed. This review consolidates current advances while identifying gaps and opportunities for future developments in automatic defect detection during radiographic testing of casting products. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Insight: Non-Destructive Testing & Condition Monitoring is the property of British Institute of Non-Destructive Testing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1784/insi.2026.68.6.389 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 389 Subjects: – SubjectFull: Radiographic processing Type: general – SubjectFull: Metal castings Type: general – SubjectFull: Image processing Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Nondestructive testing Type: general – SubjectFull: Outlier detection Type: general – SubjectFull: Industrial applications Type: general Titles: – TitleFull: Automatic defect detection in radiographic images of casting components: a comprehensive review. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kumar, S. – PersonEntity: Name: NameFull: Rani, B. Sheela IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13542575 Numbering: – Type: volume Value: 68 – Type: issue Value: 6 Titles: – TitleFull: Insight: Non-Destructive Testing & Condition Monitoring Type: main |
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