28 September 2016 Time-space analysis in photoelasticity images using recurrent neural networks to detect zones with stress concentration
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Abstract
Digital photoelasticity is based on image analysis techniques to describe the stress distribution in birefringent materials subjected to mechanical loads. However, optical assemblies for capturing the images, the steps to extract the information, and the ambiguities of the results limit the analysis in zones with stress concentrations. These zones contain stress values that could produce a failure, making important their identification. This paper identifies zones with stress concentration in a sequence of photoelasticity images, which was captured from a circular disc under diametral compression. The capturing process was developed assembling a plane polariscope around the disc, and a digital camera stored the temporal fringe colors generated during the load application. Stress concentration zones were identified modeling the temporal intensities captured by every pixel contained into the sequence. In this case, an Elman artificial recurrent neural network was trained to model the temporal intensities. Pixel positions near to the stress concentration zones trained different network parameters in comparison with pixel positions belonging to zones of lower stress concentration.
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Juan C. Briñez de León, Juan C. Briñez de León, Alejandro Restrepo M., Alejandro Restrepo M., John W. Branch, John W. Branch, "Time-space analysis in photoelasticity images using recurrent neural networks to detect zones with stress concentration", Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 99712P (28 September 2016); doi: 10.1117/12.2237373; https://doi.org/10.1117/12.2237373
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