StressNet: A deep convolutional neural network for recovering the stress field from isochromatic images

Juan C. Briñez de León; Mateo Rico-Garcia; John W. Branch; Alejandro Restrepo-M.

doi:10.1117/12.2568609

21 August 2020 StressNet: A deep convolutional neural network for recovering the stress field from isochromatic images

Juan C. Briñez de León, Mateo Rico-Garcia, John W. Branch, Alejandro Restrepo-M.

Proceedings Volume 11510, Applications of Digital Image Processing XLIII; 115100R (2020) https://doi.org/10.1117/12.2568609
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

Extending photoelasticity studies to industrial applications is a complex process generally limited by the image acquisition assembly and the computational methods for demodulating the stress field wrapped into the color fringe patterns. In response to such drawbacks, this paper proposes an auto-encoder based on deep convolutional neural networks, called StressNet, to recover the stress map from one single isochromatic image. In this case, the public dataset of synthetic photoelasticity images `Isochromatic-art' was used for training and testing achieving an averaged performance of 0.95 +/- 0.04 according to the structural similarity index. With these results, the proposed network is capable of obtaining a continuous stress surface which represents a great opportunity toward developing real time stress evaluations.

Conference Presentation

Citation Download Citation

Juan C. Briñez de León, Mateo Rico-Garcia, John W. Branch, and Alejandro Restrepo-M. "StressNet: A deep convolutional neural network for recovering the stress field from isochromatic images", Proc. SPIE 11510, Applications of Digital Image Processing XLIII, 115100R (21 August 2020); https://doi.org/10.1117/12.2568609

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