A. S. Shleenkov, O. A. Bulychev, S. A. Shleenkov, D. V. Novgorodov
THE UMD-101MK FLAW DETECTION EQUIPMENT FOR AUTOMATED MAGNETIC INSPECTION OF ELECTRICALLY WELDED SMALL AND MEDIUM PIPES OVER THE ENTIRE WALL THICKNESS AND PERIMETER
DOI: 10.17804/2410-9908.2019.6.087-101 In this paper, a multichannel magnetoresistive signal measurement and processing system for a magnetic flaw detector is developed and investigated. On this basis, a new compact flaw detection complex is created and implemented in industry for automated continuous magnetic monitoring and identification of defects in small- and medium-diameter electrowelded pipes during production over the entire wall thickness, i.e. the entire volume. The optimal structural scheme and the distinctive features of the new automated device meeting modern requirements are considered. It is shown that the use of computer technology and high-resolution thin-film matrix transducers produced according to the advanced technology of manufacturing AMRD sensors significantly increases the functionality of the flaw detector and provides detection of not only metal discontinuities and edge shifts, but also defects caused by the violation of welding conditions, e.g. cracks, fistulas, burns or adhesion of edges in the weld area. The system based on single-chip thin-film matrix transducers of the new generation is highly sensitive and makes it possible to detect both surface and bulk defects in a contactless manner with a large gap and high speed.
Acknowledgment: The work was performed under the state assignment from FASO Russia, theme
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Article reference
The Umd-101mk Flaw Detection Equipment for Automated Magnetic Inspection of Electrically Welded Small and Medium Pipes over the Entire Wall Thickness and Perimeter / A. S. Shleenkov, O. A. Bulychev, S. A. Shleenkov, D. V. Novgorodov // Diagnostics, Resource and Mechanics of materials and structures. -
2019. - Iss. 6. - P. 87-101. - DOI: 10.17804/2410-9908.2019.6.087-101. -
URL: http://eng.dream-journal.org/issues/2019-6/2019-6_267.html (accessed: 12/21/2024).
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