Book•
Digital Photoelasticity: Advanced Techniques and Applications
01 Jan 2000-
TL;DR: In this article, phase shifting, Polarization Stepping and Fourier Transform Methods are used for phase unwrapping and Optically Enhanced Tiling in digital photoelasticity.
Abstract: Transmission Photoelasticity.- Reflection Photoelasticity.- Digital Image Processing.- Fringe Multiplication.- Fringe Thinning and Fringe Clustering.- Phase Shifting, Polarization Stepping and Fourier Transform Methods.- Phase Unwrapping and Optically Enhanced Tiling in Digital Photoelasticity.- Colour Image Processing Techniques.- Evaluation of Contact Stress Parameters and Fracture Parameters.- Stress Separation Techniques.- Fusion of Digital Photoelasticity, Rapid Prototyping and Rapid Tooling Technologies.- Recent Developments and Future Trends.
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TL;DR: Deep learning-enabled optical metrology is a kind of data-driven approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances as discussed by the authors .
Abstract: Abstract With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, nondestructive testing, experimental mechanics, and biomedicine. In recent years, deep learning, a subfield of machine learning, is emerging as a powerful tool to address problems by learning from data, largely driven by the availability of massive datasets, enhanced computational power, fast data storage, and novel training algorithms for the deep neural network. It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology. Unlike the traditional “physics-based” approach, deep-learning-enabled optical metrology is a kind of “data-driven” approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances. In this review, we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology. We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning, followed by a comprehensive review of its applications in various optical metrology tasks, such as fringe denoising, phase retrieval, phase unwrapping, subset correlation, and error compensation. The open challenges faced by the current deep-learning approach in optical metrology are then discussed. Finally, the directions for future research are outlined.
165 citations
TL;DR: In this article, a review of the durability of Fresnel lenses used in the concentrating photovoltaic (CPV) application is reviewed from the literature, which primarily concerns monolithic lenses constructed of poly(methyl methacrylate) (PMMA), with supplemental examination of silicone-on-glass (SOG) composite lenses.
161 citations
TL;DR: In this paper, the authors employed 3D frozen stress and photoelastic technologies to characterize and visualize the stress distribution within the fractured coal under uniaxial compression and 3D printed model presented the fracture structures identical to those of the natural coal.
Abstract: Accurate characterization and visualization of the complex inner structure and stress distribution of rocks are of vital significance to solve a variety of underground engineering problems. In this paper, we incorporate several advanced technologies, such as CT scan, three-dimensional (3D) reconstruction, and 3D printing, to produce a physical model representing the natural coal rock that inherently contains complex fractures or joints. We employ 3D frozen stress and photoelastic technologies to characterize and visualize the stress distribution within the fractured rock under uniaxial compression. The 3D printed model presents the fracture structures identical to those of the natural prototype. The mechanical properties of the printed model, including uniaxial compression strength, elastic modulus, and Poissons ratio, are testified to be similar to those of the prototype coal rock. The frozen stress and photoelastic tests show that the location of stress concentration and the stress gradient around the discontinuous fractures are in good agreement with the numerical predictions of the real coal sample. The proposed method appears to be capable of visually quantifying the influences of discontinuous, irregular fractures on the strength, deformation, and stress concentration of coal rock. The method of incorporating 3D printing and frozen stress technologies shows a promising way to quantify and visualize the complex fracture structures and their influences on 3D stress distribution of underground rocks, which can also be used to verify numerical simulations.
130 citations
TL;DR: An overview of the principal techniques of digital fringe processing is provided within a single theoretical framework in this paper, where experiments involving more I x 10 6 quantitative fringe order measurements are possible and practical on a routine basis using the current technology.
Abstract: The enormously enhanced power of photoelasticity resulting from adoption of digital technologies is highlighted and discussed. An overview of the principal techniques of digital fringe processing is provided within a single theoretical framework. The practical application of the new technologies using both conventional instruments and novel optical devices is discussed. Experiments involving more I x 10 6 quantitative fringe order measurements are possible and practical on a routine basis using the current technology. Products based on this research are beginning to appear on the market so that many new application areas are opening up for photoelasticity, such as dynamic events, real-time fatigue crack analysis, monitoring polarisation changes at a microscopic level in materials; detailed validation of numerical simulations, particularly of complex geometry and loading; and in-service monitoring using reflection photoelasticity of damage in both homogeneous and heterogeneous materials, such as composites.
106 citations
Cites background from "Digital Photoelasticity: Advanced T..."
...Digital photoelasticity is a subject that is maturing, as evidenced by the publication of the first monograph dedicated to the topic [3], by the prototypes entering into industrial usage, and by systems appearing on the market....
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...A variety of techniques has been published with the common aim of delivering fast and accurate photoelastic fringe analysis [3]....
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TL;DR: Deep learning-enabled optical metrology is a kind of data-driven approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances as discussed by the authors .
Abstract: Abstract With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, nondestructive testing, experimental mechanics, and biomedicine. In recent years, deep learning, a subfield of machine learning, is emerging as a powerful tool to address problems by learning from data, largely driven by the availability of massive datasets, enhanced computational power, fast data storage, and novel training algorithms for the deep neural network. It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology. Unlike the traditional “physics-based” approach, deep-learning-enabled optical metrology is a kind of “data-driven” approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances. In this review, we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology. We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning, followed by a comprehensive review of its applications in various optical metrology tasks, such as fringe denoising, phase retrieval, phase unwrapping, subset correlation, and error compensation. The open challenges faced by the current deep-learning approach in optical metrology are then discussed. Finally, the directions for future research are outlined.
95 citations