Reliability of Dye Penetrant Inspection Method to Detect Weld Discontinuities.
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| Title: | Reliability of Dye Penetrant Inspection Method to Detect Weld Discontinuities. |
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| Authors: | Vera, J.1 (AUTHOR) jvera@unitru.edu.pe, Caballero, L.1 (AUTHOR), Taboada, M.1 (AUTHOR) |
| Source: | Russian Journal of Nondestructive Testing. Jan2024, Vol. 60 Issue 1, p85-95. 11p. |
| Subjects: | Welding, Welded joints, Welding defects, Fluorescent dyes, Test systems, Dyes & dyeing, Structural components |
| Abstract: | The dye penetrant inspection method used to reveal surface weld discontinuities is an important factor for quality verification in the manufacture of structural components; however, it is probable that certain size-dependent discontinuities may or may not be detected. Then, how reliable can it be? In this sense, the objective of the research has been to estimate the reliability of dye penetrant inspection to detect discontinuities in relation to their size. Six experimental tests were performed by three inspectors, with visible and fluorescent dye penetrants, on twenty welded joints of similar surface characteristics, containing 63 typical weld discontinuities arranged according to shape and size, whereas POD reliability quantitative estimates were developed by the hit-or-miss statistical method. For the test system, the fluorescent penetrants, due to their greater sensitivity compared to the visible ones, registered greater reliability in revealing smaller discontinuities. The POD estimators were a50 (1.469 mm < 1.978 mm), a90 (6.348 mm < 7.474 mm), a90/95 (14.58 mm < 15.77 mm). Fluorescent dyes allowed a higher rate and probability of detection; both factors showed a tendency to increase as the discontinuities size increased. [ABSTRACT FROM AUTHOR] |
| Copyright of Russian Journal of Nondestructive Testing is the property of Springer Nature 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 |
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