DOI:
https://doi.org/10.14483/22484728.12789Published:
2017-06-28Issue:
Vol. 11 No. 1 (2017)Section:
A Research VisionAnálisis de resolución en imágenes de fotoelasticidad: caso carga dinámica
analysis of resolution on photoelasticity images: dynamic load case
Keywords:
Campos de esfuerzos, concentración de esfuerzos, densidad de franjas, fotoelasticidad digital, imágenes sintéticas (es).Keywords:
Stress field, digital photoelasticity, stress concentration, fringe density, synthetic images (en).Downloads
Abstract (es)
En imágenes de fotoelasticidad, los patrones de franjas envuelven el campo de esfuerzos de un cuerpo birrefringente bajo cargas mecánicas. En estos casos, zonas con concentraciones de esfuerzos producen altas densidades de franjas, dificultando la recuperación, y en ocasiones la pérdida, de la información envuelta en la imagen. Este artículo evalúa el crecimiento de tales regiones y su efecto en el campo de esfuerzos para un caso donde el cuerpo es sometido a cargas dinámicas. Aquí, una secuencia sintética de imágenes de fotoelasticidad es generada partiendo del modelo analítico de la distribución de esfuerzos en un disco bajo compresión diametral. El tamaño de las regiones con pérdida de información es estimado mediante la implementación de transformadas de Fourier y filtros paso alto. Los resultados muestran que el incremento de la carga introduce aumentos en las regiones con pérdida de información. En estos casos, los esfuerzos máximos recuperados a partir de las imágenes se alejan de los valores en el modelo analítico. Esto permite identificar regiones en las imágenes de fotoelasticidad, donde el campo de esfuerzos debe ser corregido
Abstract (en)
In photoelasticity images, fringe patterns wrap the stress field in birefringent bodies under mechanical loads. In these cases, high stress concentrations produce zones with high fringe densities, which makes difficult to recover the information wrapped by the image. This paper evaluates the size of such regions, and their effect into the stress field for a case with dynamic loads. Here, a synthetic sequence of photoelasticity images is generated by modeling the stress distribution on a disc under diametric compression. The size of regions with limitations is estimated by implementing the discrete Fourier transform and high pass filters. Our results show that increasing the load values, the size of the regions gets bigger. In these cases, the maximum stress one can recover does not match with the values in the analytical model. This allow to identify regions in the photoelasticity images, where the stress field must be corrected.
References
E. Patterson y Z. Wang, “Towards full field automated photoelastic analysis of complex components,” Strain, vol. 27, no. 2, pp. 49–53, 1991, https://doi.org/10.1111/j.1475-1305.1991.tb00752.x
K. Ramesh y V. Ramakrishnan, “Digital photoelasticity of glass: A comprehensive review,” Optics and Lasers in Engineering, vol. 87, pp. 59–74, 2016, https://doi.org/10.1016/j.optlaseng.2016.03.017
E. A. Patterson, “Digital photoelasticity: principles, practice and potential,” Strain, vol. 38, no. 1, pp. 27–39, 2002, https://doi.org/10.1046/j.0039-2103.2002.00004.x
A. Asundi, “Phase shifting in photoelasticity,” Experimental Techniques, vol. 17, no. 1, pp. 19–23, 1993, https://doi.org/10.1111/j.1747-1567.1993.tb00269.x
K. Ramesh y V. Ganapathy, “Phase-shifting methodologies in photoelastic analysis the application of jones calculus,” The Journal of Strain Analysis for Engineering Design, vol. 31, no. 6, pp. 423–432, 1996, https://doi.org/10.1243/03093247V316423
M. Ramji y K. Ramesh, “Whole field evaluation of stress components in digital photoelasticityissues, implementation and application,” Optics and Lasers in Engineering, vol. 46, no. 3, pp. 257–271, 2008, https://doi.org/10.1016/j.optlaseng.2007.09.006
G. S. Grewal y V. N. Dubey, “Inverse problem of photoelastic fringe mapping using neural networks,” Measurement Science and Technology, vol. 18, no. 5, p. 1361, 2007, https://doi.org/10.1088/0957-0233/18/5/024
J. Quiroga y A. Gonzalez-Cano, “Separation of isoclinics and isochromatics from photoelastic data with a regularized phase-tracking technique,” Applied optics, vol. 39, no. 17, pp. 2931–2940, 2000, https://doi.org/10.1364/AO.39.002931
W. Shang, X. Ji, y X. Yang, “Study on several problems of automatic full-field isoclinic parameter measurement by digital phase shifting photoelasticity,” Optik - International Journal for Light and Electron Optics, vol. 126, no. 19, pp. 1981–1985, 2015.
K. Ramesh y S. Mangal, “Data acquisition techniques in digital photoelasticity: a review,” Optics and lasers in engineering, vol. 30, no. 1, pp. 53–75, 1998, https://doi.org/10.1016/S0143-8166(97)00105-X
J. Briñez de Leon, A. Restrepo, y J. W. Branch, “Time-space analysis in photoelasticity images using recurrent neural networks to detect zones with stress concentration,” in Applications of Digital Image Processing XXXIX, vol. 9971. International Society for Optics and Photonics, 2016, p. 99712P.
J. C. Briñez de Leon, A. Restrepo, y J. W. Branch, “High stress concentration analysis using rgb intensity changes in dynamic
photoelasticity videos,” in Signal Processing, Images and Artificial Vision (STSIVA), 2016 XXI Symposium on. IEEE, 2016, pp. 1–7.
P. M. Junior, F. Vieira, C. Magalhaes, J. Ribeiro, y I. Rios, “Numerical method to digital photoelasticity using plane polariscope,” Optics express, vol. 24, no. 12, pp. 12 617–12 624, 2016.
C. Erik, Z. Daniel, y P. Marco, “Procesamiento digital de imágenes con matlab y simulink,” RA-MA Editorial, México, 2010.
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