PhotoelastNet: a deep convolutional neural network for evaluating the stress field by using a single color photoelasticity image

光弹性 计算机科学 卷积神经网络 人工智能 压力(语言学) 应力场 领域(数学) 过程(计算) 公制(单位) 像素 计算机视觉 光学 模式识别(心理学) 数学 工程类 有限元法 结构工程 物理 柯西应力张量 数学分析 语言学 哲学 运营管理 纯数学 操作系统
作者
Juán León,Mateo Rico-García,Alejandro Restrepo-Martínez
出处
期刊:Applied Optics [The Optical Society]
卷期号:61 (7): D50-D50 被引量:21
标识
DOI:10.1364/ao.444563
摘要

Quantifying the stress field induced into a piece when it is loaded is important for engineering areas since it allows the possibility to characterize mechanical behaviors and fails caused by stress. For this task, digital photoelasticity has been highlighted by its visual capability of representing the stress information through images with isochromatic fringe patterns. Unfortunately, demodulating such fringes remains a complicated process that, in some cases, depends on several acquisitions, e.g., pixel-by-pixel comparisons, dynamic conditions of load applications, inconsistence corrections, dependence of users, fringe unwrapping processes, etc. Under these drawbacks and taking advantage of the power results reported on deep learning, such as the fringe unwrapping process, this paper develops a deep convolutional neural network for recovering the stress field wrapped into color fringe patterns acquired through digital photoelasticity studies. Our model relies on an untrained convolutional neural network to accurately demodulate the stress maps by inputting only one single photoelasticity image. We demonstrate that the proposed method faithfully recovers the stress field of complex fringe distributions on simulated images with an averaged performance of 92.41% according to the SSIM metric. With this, experimental cases of a disk and ring under compression were evaluated, achieving an averaged performance of 85% in the SSIM metric. These results, on the one hand, are in concordance with new tendencies in the optic community to deal with complicated problems through machine-learning strategies; on the other hand, it creates a new perspective in digital photoelasticity toward demodulating the stress field for a wider quantity of fringe distributions by requiring one single acquisition.
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