Automated Processing of Data from Measurement of Thermo-Electromotive Voltage and Magnetic Noise Voltage in Metals and Alloys
Source abstract
Abstract
Abstract The research analyses some possibilities for automated processing of data from measuring the nondestructive testing characteristics: thermo-electromotive force (EMF) and magnetic noise (MN), also known as Barkhausen noise (NB), represented respectively by the parameters: thermo-electromotive voltage ET and magnetic noise voltage ENB, in samples of metals and alloys. Mathematical algorithms for developing software for an automated system for evaluating the test results are presented. Two statistical methods for classifying groups of samples and products according to measured values of ET and ENB, when sorting by grade (chemical composition) and heat treatment (hardness) of structural steels were investigated—cluster analysis and robust (stable) evaluations, which are suitable for data processing with high dispersion.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Elasticity and Wave Propagation · Material Properties and Failure Mechanisms · Engineering Diagnostics and Reliability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (15)
Show source-provided references
1. Duran, B. and Odell, P., Cluster Analysis, Berlin: Springer, 1974.
2. Classification and Clustering, van Ryzin, J., Ed., Cambridge: Academic, 1976. Translated under the title Klassifikatsiya i Klaster, Moscow: Mir, Moscow, 1978.
3. Gorelik, A.L. and Skripkin, V.A., Metodi raspoznovaniya (Recognition Methods), Moscow: Mir, 1984.
4. Enyutkov, I.S., Prikladnoe programmnoe obespechenie mnogomernoi statistiki (Applied Software for Multidimensional Statistics), Moscow: Mir, 1986.
5. Afifi, A. and Azen, S., Statistical Analysis, a Computer Oriented Approach, New York: Academic, 1982.
6. Kikuchi, M., Dental alloy sorting by the thermoelectric method, Eur. J. Dent., 2010, vol. 4, no. 1, pp. 66–70.10.1055/s-0039-1697810 ↗
7. Kidd, M.L., Watch out for those thermoelectric voltages!, Int. J. Metrol., 2012, pp. 18–21.
8. Kostin, V., Vasilenko, O., Filatenkov, D., Chekasina, Yu., and Serbin, E., Magnetic and magnetoacoustic testing parameters of the stressed–strained state of carbon steels that were subjected to a cold plastic deformation and annealing, Russ. J. Nondestr. Test., 2015, vol. 51, no. 8, pp. 624–632.10.1134/S1061830915100071 ↗
9. Ivanova, Y., Nondestructive monitoring of tensile of mild steel samples by magnetic Barkhäusen and ultrasonic methods, MATEC Web Conf., 2018, vol. 145, p. 05007.10.1051/matecconf/201814505007 ↗
10. Dzudzev, B., Angelov, V., Zlatkov, M., and Kostadinov, P., Testing of automatized system for complex nondestructive study of metallic materials, Mach. Technol. Mater., 2019, no. 6, pp. 273–276.
11. Kamenov, V. and Mirchev, Y., Metrological provision of Multitest CD010 device for measuring the velocity and attenuation of ultrasound in materials, NDT Days, 2019, vol. 2, no. 4, pp. 156–158.
12. ASTM E977-05, Standard practice for thermoelectric sorting of electrically conductive materials, 2014. Last Updated: Jan 09, 2020.
13. Velev, B., Ivanov,I., and Kamenov, V., Automated system for complex nondestructive testing of the structure and mechanical properties of mechanical engineering materials, Russ. J. Nondestr. Test., 2021, vol. 57, no. 3, pp. 203–211.10.1134/S1061830921030074 ↗
14. Veber, V. and Dilman, V., Improved metrological characteristics of eddy current testing, Int. Conf. Industr. Eng. Appl. Manuf., 2021, pp. 389–392.10.1109/ICIEAM51226.2021.9446303 ↗
15. Gorkunov, E.S., Povolotskaya, A.M., Zadvorkin, S.M., Putilova, E.A., and Mushnikov, A.N., The effect of cyclic preloading on the magnetic behavior of the hot-rolled 08G2B steel under elastic uniaxial tension, Res. Nondestr. Eval., 2021, vol. 32, no. 6, pp. 276–294. https://doi.org/10.1080/09349847.2021.200248710.1080/09349847.2021.2002487 ↗