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Volume 22
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10.3390/s22186852
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Article

Machine Vision-Based Fatigue Crack Propagation System †

by
Jan Gebauer
*,
Pavel Šofer
,
Martin Jurek
,
Renata Wagnerová
and
Jiří Czebe
Department of Control Systems and Instrumentation, VŠB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our conference paper: Gebauer. J., Šofer, F., Jurek, M. “The System for Fatigue Crack Propagation Detection Based on Machine Vision.” Proceedings of 2021 22nd International Carpathian Control Conference (ICCC), Velké Karlovice, Czech Republic, 31 May−1 June 2021.
Sensors 2022, 22(18), 6852; https://doi.org/10.3390/s22186852
Submission received: 11 August 2022 / Revised: 5 September 2022 / Accepted: 7 September 2022 / Published: 10 September 2022
(This article belongs to the Section Fault Diagnosis & Sensors)
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Abstract

This paper introduces a machine vision-based system promising low-cost solution for detecting a fatigue crack propagation caused by alternating mechanical stresses. The fatigue crack in technical components usually starts on surfaces at stress concentration points. The presented system was designed to substitute a strain gauge sensor-based measurement using an industrial camera in cooperation with branding software. This paper presents implementation of a machine vision system and algorithm outputs taking on fatigue crack propagation samples.
Keywords: crack; propagation; surface crack; machine vision; National Instruments; Vision Builder crack; propagation; surface crack; machine vision; National Instruments; Vision Builder

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MDPI and ACS Style

Gebauer, J.; Šofer, P.; Jurek, M.; Wagnerová, R.; Czebe, J. Machine Vision-Based Fatigue Crack Propagation System. Sensors 2022, 22, 6852. https://doi.org/10.3390/s22186852

AMA Style

Gebauer J, Šofer P, Jurek M, Wagnerová R, Czebe J. Machine Vision-Based Fatigue Crack Propagation System. Sensors. 2022; 22(18):6852. https://doi.org/10.3390/s22186852

Chicago/Turabian Style

Gebauer, Jan, Pavel Šofer, Martin Jurek, Renata Wagnerová, and Jiří Czebe. 2022. "Machine Vision-Based Fatigue Crack Propagation System" Sensors 22, no. 18: 6852. https://doi.org/10.3390/s22186852

APA Style

Gebauer, J., Šofer, P., Jurek, M., Wagnerová, R., & Czebe, J. (2022). Machine Vision-Based Fatigue Crack Propagation System. Sensors, 22(18), 6852. https://doi.org/10.3390/s22186852

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