Strain measurement during tensile testing using deep learning-based digital image correlation

Min, Hyeon-Gyu; On, H.; Kang, Dong-Joong; Park, Jun-Hyub

doi:10.1088/1361-6501/ab29d5

Cited by 20 publications

(8 citation statements)

References 29 publications

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“…Their experimental results demonstrated the feasibility of the deep-learning approach for accurate pixel-wise subpixel measurement over full displacement fields. Min et al 411 proposed a 3D CNN-based strain measurement method, which allowed simultaneous characterization in spatial and temporal domains from the surface images obtained during a tensile test of BeCu thin film. Rezaie et al 412 compared the performance of conventional DIC method and their deep-learning method based on U-Net for detecting cracks on stone masonry wall images, showing that the learning-based method could detect most visible cracks and better preserve the crack geometry.…”

Section: The Use Of Deep Learning In Optical Metrologymentioning

confidence: 99%

Deep learning in optical metrology: a review

et al. 2022

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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.

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Section: The Use Of Deep Learning In Optical Metrologymentioning

confidence: 99%

Deep learning in optical metrology: a review

et al. 2022

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“…As indicated by this, the coarse-fine search calculation is appropriate for such an assignment. Reference [ 11 ] shows that, by applying deep learning image correlation, the elastic modulus of nonlinear deformation of images gives the better result with some time consumption. From outset, it figures the simulated correlation value to every focal point in looking through the zone containing a 1-pixel subset.…”

Section: Related Workmentioning

confidence: 99%

Accurate Measurement of Strain in Noncontact Surface Deformation Using Subset-Based Digital Image Correlation

Shifani

Premi

2021

Contrast Media & Molecular Imaging

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The measurement of strain using some contact techniques has some drawbacks like less accuracy and it takes larger computation time for finding each location of subpixels. Thus, a faster noncontact Digital Image Correlation (DIC) mechanism is utilized along with the traditional techniques to measure the strain. The Newton-Raphson (NR) technique is considered to be an accepted mechanism for accurate tracking of different intensity relocation. Generally, the issue regarding the DIC mechanism is its computational cost. In this paper, an interpolation technique is utilized to accomplish a high precision rate and faster image correlation; thereby it reduces the computation time required for finding the matched pixel and viably handles the rehashing relationship process. Hence, the proposed mechanism provides better efficiency along with a reduced number of iterations required for finding the identity. The number of iterations can be reduced using the Sum of Square of Subset Intensity Gradients (SSSIG) method. The evaluation of the projected scheme is tested with different images through various parameters. Finally, the outcome indicates that the projected mechanism takes only a few milliseconds to match the best matching location, whereas the prevailing techniques require 16 seconds for the same operation with the same step size. This demonstrates the effectiveness of the proposed scheme.

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“…As indicated by this, the coarse-fine search calculation is appropriate for such an assignment. (Min et al 2020) shows that, by applying deep learning image correlation, the elastic modulus of non-linear deformation of images gives the better result with some time-consumption. From outset, it figures the pretend correlation value to every focal point in looking through the zone contain a 1-pixel subset.…”

Section: Related Workmentioning

confidence: 99%

Accurate Measurement of Strain in Non-Contact Surface Deformation Using Subset Based Digital Image Correlation

Gpm

2021

Preprint

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The measurement of strain using some contact techniques has some drawbacks like less accuracy and it takes larger computation time for finding each location of sub-pixels. Thus, a faster non-contact Digital Image Correlation (DIC) mechanism is utilized along with the traditional techniques to measure the strain. The Newton-Raphson (NR) technique is considered to be an accepted mechanism for accurate tracking of different intensity relocation. Generally, the issue regarding the DIC mechanism is its computational cost. In this paper an interpolation technique is utilized to accomplish a high precision rate and faster image correlation, thereby it reduces the computation time required for finding the matched pixel and viably handles the rehashing relationship process. Hence the proposed mechanism provides better efficiency along with a reduced number of iterations required for finding the identity. The number of iteration can be reduced using the Sum of Square of Subset Intensity Gradient (SSSIG) method. The evaluation of the projected scheme is tested with different images through various parameters. Finally, the outcome indicates that the projected mechanism takes only a few milliseconds to match the best matching location whereas the prevailing techniques require 16 seconds for the same operation with the same step size. This demonstrates the effectiveness of the proposed scheme.

show abstract

Strain measurement during tensile testing using deep learning-based digital image correlation

Cited by 20 publications

References 29 publications

Deep learning in optical metrology: a review

Deep learning in optical metrology: a review

Accurate Measurement of Strain in Noncontact Surface Deformation Using Subset-Based Digital Image Correlation

Accurate Measurement of Strain in Non-Contact Surface Deformation Using Subset Based Digital Image Correlation

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