Inverse problem of photoelastic fringe mapping using neural networks
Inverse problem of photoelastic fringe mapping using neural networks
复制标题
使用神经网络的光弹性条纹映射反问题
DOI：
10.1088/0957-0233/18/5/024
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
V. Dubey
中科院分区：
文献类型：
--
作者：
G. Grewal;V. Dubey
本文提出了一种利用神经网络确定载荷的光弹性条纹反分析方法。该技术可能是有用的，在全场分析的光弹性图像获得由于外部负载，这可能会发现应用在各种专业领域，包括机器人和生物医学工程。所提出的技术易于实现，不需要太多的计算，并可以科普轻微的实验变化。该技术需要图像采集、滤波和数据提取，然后将其馈送到神经网络以提供负载作为输出。这种技术可以有效地实施，以确定在重复加载是主要考虑因素之一的应用中施加的负载。本文提出的结果表明，这种技术的新奇，以解决从直接图像数据的反问题。它已被证明，所提出的技术提供了更好的结果比以前发表的作品的逆光弹性问题。
This paper presents an enhanced technique for inverse analysis of photoelastic fringes using neural networks to determine the applied load. The technique may be useful in whole-field analysis of photoelastic images obtained due to external loading, which may find application in a variety of specialized areas including robotics and biomedical engineering. The presented technique is easy to implement, does not require much computation and can cope well within slight experimental variations. The technique requires image acquisition, filtering and data extraction, which is then fed to the neural network to provide load as output. This technique can be efficiently implemented for determining the applied load in applications where repeated loading is one of the main considerations. The results presented in this paper demonstrate the novelty of this technique to solve the inverse problem from direct image data. It has been shown that the presented technique offers better result for the inverse photoelastic problems than previously published works.