Automated Defect Detection through Flaw Grading in Non-Destructive Testing Digital X-ray Radiography
Process automation utilizes specialized technology and equipment to automate and enhance production processes, leading to higher manufacturing efficiency, higher productivity, and cost savings. The aluminum die casting industry has significantly gained from the implementation of process automation s...
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MDPI AG
2024-10-01
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| Series: | NDT |
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| Online Access: | https://www.mdpi.com/2813-477X/2/4/23 |
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| author | Bata Hena Gabriel Ramos Clemente Ibarra-Castanedo Xavier Maldague |
| author_facet | Bata Hena Gabriel Ramos Clemente Ibarra-Castanedo Xavier Maldague |
| author_sort | Bata Hena |
| collection | DOAJ |
| description | Process automation utilizes specialized technology and equipment to automate and enhance production processes, leading to higher manufacturing efficiency, higher productivity, and cost savings. The aluminum die casting industry has significantly gained from the implementation of process automation solutions in manufacturing, serving safety-critical sectors such as automotive and aerospace industries. However, this method of component fabrication is very susceptible to generating manufacturing flaws, hence necessitating adequate non-destructive testing (NDT) to ascertain the fitness for use of such components. Machine learning has taken the center stage in recent years as a tool for developing automated solutions for detecting and classifying flaws in digital X-ray radiography. These machine learning-based solutions have increasingly been developed and deployed for component inspection, to keep pace with the high production throughput in manufacturing industries. This work focuses on the development of a defect grading algorithm that assesses detected flaws to ascertain if they constitute a defect that could render a component unfit for use. Guided by ASTM 2973-15; Standard Digital Reference Images for Inspection of Aluminum and Magnesium Die Castings, a grading pipeline utilizing K-D (k-dimensional) trees was developed to effectively structure detected flaws, enabling the system to make decisions based on acceptable grading terms. This solution is dynamic in terms of its conformity to different grading criteria and offers the possibility to achieve automated decision making (Accept/Reject) in digital X-ray radiography applications. |
| format | Article |
| id | doaj-art-0ed9eef822934b5ba9cfe30081875c95 |
| institution | Kabale University |
| issn | 2813-477X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | NDT |
| spelling | doaj-art-0ed9eef822934b5ba9cfe30081875c952025-01-24T13:44:19ZengMDPI AGNDT2813-477X2024-10-012437839110.3390/ndt2040023Automated Defect Detection through Flaw Grading in Non-Destructive Testing Digital X-ray RadiographyBata Hena0Gabriel Ramos1Clemente Ibarra-Castanedo2Xavier Maldague3Department of Electrical and Computer Engineering, 1065, Ave de la Médecine, Université Laval, Quebec City, QC G1V 0A6, CanadaDepartment of Computer Science and Software Engineering, Moncton, Université Laval, Quebec City, QC G1V 0A6, CanadaDepartment of Electrical and Computer Engineering, 1065, Ave de la Médecine, Université Laval, Quebec City, QC G1V 0A6, CanadaDepartment of Electrical and Computer Engineering, 1065, Ave de la Médecine, Université Laval, Quebec City, QC G1V 0A6, CanadaProcess automation utilizes specialized technology and equipment to automate and enhance production processes, leading to higher manufacturing efficiency, higher productivity, and cost savings. The aluminum die casting industry has significantly gained from the implementation of process automation solutions in manufacturing, serving safety-critical sectors such as automotive and aerospace industries. However, this method of component fabrication is very susceptible to generating manufacturing flaws, hence necessitating adequate non-destructive testing (NDT) to ascertain the fitness for use of such components. Machine learning has taken the center stage in recent years as a tool for developing automated solutions for detecting and classifying flaws in digital X-ray radiography. These machine learning-based solutions have increasingly been developed and deployed for component inspection, to keep pace with the high production throughput in manufacturing industries. This work focuses on the development of a defect grading algorithm that assesses detected flaws to ascertain if they constitute a defect that could render a component unfit for use. Guided by ASTM 2973-15; Standard Digital Reference Images for Inspection of Aluminum and Magnesium Die Castings, a grading pipeline utilizing K-D (k-dimensional) trees was developed to effectively structure detected flaws, enabling the system to make decisions based on acceptable grading terms. This solution is dynamic in terms of its conformity to different grading criteria and offers the possibility to achieve automated decision making (Accept/Reject) in digital X-ray radiography applications.https://www.mdpi.com/2813-477X/2/4/23non-destructive testingdigital X-ray radiographymachine learningautomated defect recognitiondefect gradingK-D tree |
| spellingShingle | Bata Hena Gabriel Ramos Clemente Ibarra-Castanedo Xavier Maldague Automated Defect Detection through Flaw Grading in Non-Destructive Testing Digital X-ray Radiography NDT non-destructive testing digital X-ray radiography machine learning automated defect recognition defect grading K-D tree |
| title | Automated Defect Detection through Flaw Grading in Non-Destructive Testing Digital X-ray Radiography |
| title_full | Automated Defect Detection through Flaw Grading in Non-Destructive Testing Digital X-ray Radiography |
| title_fullStr | Automated Defect Detection through Flaw Grading in Non-Destructive Testing Digital X-ray Radiography |
| title_full_unstemmed | Automated Defect Detection through Flaw Grading in Non-Destructive Testing Digital X-ray Radiography |
| title_short | Automated Defect Detection through Flaw Grading in Non-Destructive Testing Digital X-ray Radiography |
| title_sort | automated defect detection through flaw grading in non destructive testing digital x ray radiography |
| topic | non-destructive testing digital X-ray radiography machine learning automated defect recognition defect grading K-D tree |
| url | https://www.mdpi.com/2813-477X/2/4/23 |
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