Affiliation:
1. Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University
Abstract
Evaluating the stress field based on photoelasticity is of vital
significance in engineering fields. To achieve the goal of efficiently
demodulating stress distribution and to overcome the limitations of
conventional methods, it is essential to develop a deep learning
method to simplify and accelerate the process of image acquisition and
processing. A framework is proposed to enhance prediction accuracy. By
adopting Resnet as the backbone, applying U-Net architecture, and
adding a physical constraint module, our model recovers the stress
field with higher structural similarity. Under different conditions,
our model performs robustly despite complicated geometry and a large
stress range. The results prove the universality and effectiveness of
our model and offer an opportunity for instant stress detection.
Funder
Fundamental Research Funds for the
Central Universities
National College Students Innovation and
Entrepreneurship Training Program
Subject
Atomic and Molecular Physics, and Optics,Engineering (miscellaneous),Electrical and Electronic Engineering
Cited by
1 articles.
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