The mechanical fatigue is an important contributor to the failure of mechanical components. In order to avoid this condition, the phenomenon has to be predicted and controlled during the design and the implementation of a mechanical component. The mechanical fatigue can lead to maintenance, to parts replacement, to extra-needs for lubricants and ancillary labor, and it is one of the main factors of economic loss. Every mechanical component intended for the force transmission is subject to mechanical fatigue. The analysis of the system status during time enables the evaluation and characterization of the fatigue influence on its behavior. In mechanical devices performing a work-cycle with moving parts, the cyclic movements have to be “the same” during time. The main target of this paper is to verify the mechanical behavior of two different gripping blocks during long periods of high cyclic fatigue work. Both blocks have moving parts and all the acquisitions are concentrated to capture fatigue sign on devices work-cycle, mainly in terms of moving parts positioning. To perform the kinematic analysis of both devices under test, and to verify and quantify the degradation in their mechanical performance, a Motion Capture System (VICON) has been combined with an intelligent tool for imaging analysis (KINOVEA). In this way, the precision and the reliability of a free imaging analysis software applied to cyclic working conditions have been compared, on a bi dimensional plane, with data captured by a stereophotogrammetric system.

Mechanical fatigue evaluation by image recognition / Milani, M.; Montorsi, L.; Fontanili, L.; Storchi, G.; Muzzioli, G.. - 1131:(2020), pp. 1088-1093. ((Intervento presentato al convegno 3rd International Conference on Intelligent Human Systems Integration (IHSI) - Integrating People and Intelligent Systems tenutosi a Modena, ITALY nel FEB 19-21, 2020 [10.1007/978-3-030-39512-4_165].

Mechanical fatigue evaluation by image recognition

Milani M.;Montorsi L.;Fontanili L.
;
Storchi G.;Muzzioli G.
2020-01-01

Abstract

The mechanical fatigue is an important contributor to the failure of mechanical components. In order to avoid this condition, the phenomenon has to be predicted and controlled during the design and the implementation of a mechanical component. The mechanical fatigue can lead to maintenance, to parts replacement, to extra-needs for lubricants and ancillary labor, and it is one of the main factors of economic loss. Every mechanical component intended for the force transmission is subject to mechanical fatigue. The analysis of the system status during time enables the evaluation and characterization of the fatigue influence on its behavior. In mechanical devices performing a work-cycle with moving parts, the cyclic movements have to be “the same” during time. The main target of this paper is to verify the mechanical behavior of two different gripping blocks during long periods of high cyclic fatigue work. Both blocks have moving parts and all the acquisitions are concentrated to capture fatigue sign on devices work-cycle, mainly in terms of moving parts positioning. To perform the kinematic analysis of both devices under test, and to verify and quantify the degradation in their mechanical performance, a Motion Capture System (VICON) has been combined with an intelligent tool for imaging analysis (KINOVEA). In this way, the precision and the reliability of a free imaging analysis software applied to cyclic working conditions have been compared, on a bi dimensional plane, with data captured by a stereophotogrammetric system.
3rd International Conference on Intelligent Human Systems Integration (IHSI) - Integrating People and Intelligent Systems
Modena, ITALY
FEB 19-21, 2020
1131
1088
1093
Milani, M.; Montorsi, L.; Fontanili, L.; Storchi, G.; Muzzioli, G.
Mechanical fatigue evaluation by image recognition / Milani, M.; Montorsi, L.; Fontanili, L.; Storchi, G.; Muzzioli, G.. - 1131:(2020), pp. 1088-1093. ((Intervento presentato al convegno 3rd International Conference on Intelligent Human Systems Integration (IHSI) - Integrating People and Intelligent Systems tenutosi a Modena, ITALY nel FEB 19-21, 2020 [10.1007/978-3-030-39512-4_165].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1208565
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