Presentation + Paper
21 August 2020 Generalized adversarial networks for stress field recovering processes from photoelasticity images
Author Affiliations +
Abstract
For overcoming conventional photoelasticity limitations when evaluating the stress field in loaded bodies, this paper proposes a Generative Adversarial Network (GAN) while maintaining performance, gaining experimental stability, and shorting time response. Due to the absence of public photoelasticity data, a synthetic dataset was generated by using analytic stress maps and crops from them. In this case, more than 100000 pair of images relating fringe colors to their respective stress surfaces were used for learning to unwrap the stress information contained into the fringes. Main results of the model indicate its capability of recovering the stress field achieving an averaged performance of 0.93±0.18 according to the structural similarity index (SSIM). These results represent a great opportunity for exploring GAN models in real time stress evaluations.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Juan C. Briñez de León, Ruben D. Fonnegra-Tarazona, and Alejandro Restrepo-Martínez "Generalized adversarial networks for stress field recovering processes from photoelasticity images", Proc. SPIE 11510, Applications of Digital Image Processing XLIII, 115100S (21 August 2020); https://doi.org/10.1117/12.2568700
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KEYWORDS
Photoelasticity

Fringe analysis

Gallium nitride

Data modeling

Sensors

Light sources

Cameras

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