Temporal heterodyne shearing speckle pattern interferometry

Wang, Xu; Gao, Zhan; Qin, Jie; Zhang, Xiaoqiong; Yang, Shanwei

doi:10.1016/j.optlaseng.2017.01.010

Cited by 6 publications

(6 citation statements)

References 16 publications

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“…WP also does not require the shifting structure of the Michelson interferometer. In further experiments, we proved the feasibility of combining the polarization splitting effect with the electro-optical crystal materials in temporal heterodyne shearing speckle pattern interferometry [11]. However, there are still some problems to be solved.…”

Section: Introductionmentioning

confidence: 83%

Digital Shearing Speckle Pattern Interferometry Based on Rochon Prism and Its Application

Wang

Gao

et al. 2019

Applied Sciences

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This paper presents improved digital shearing speckle pattern interferometry based on Rochon Prism. The advantage of this method is that it avoids the shadow noise caused by transmission light, which improves the shearography accuracy. By reducing the angle of divergence and coordinating the measuring distance and the precision coordinate, the beam-splitting path of the interference optical path is improved. Experimental results prove that the improved method contributes to the practical and instrumental application of shearography.

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Section: Introductionmentioning

confidence: 83%

Digital Shearing Speckle Pattern Interferometry Based on Rochon Prism and Its Application

Wang

Gao

et al. 2019

Applied Sciences

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“…The intensity distribution of the CCD surface is sensitive to the gradient of the out-of-plane deformation of the measured surface. When the shear is along the z-direction, the relationship between the gradient of the out-of-plane deformation and the phase difference Δφ can be expressed as 15,16 E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 3 ; 1 1 4 ; 5 0 7…”

Section: Principlementioning

confidence: 99%

Looseness inspection of fasteners based on digital shearing speckle pattern interferometry and convolutional neural network

Deng,

Gao,

Chen

et al. 2024

Opt. Eng.

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The fastener plays a critical role in ensuring the stability and safety of modern engineering structures. However, factors such as vibration, shock, temperature changes, and others can cause fasteners to become loose, thereby compromising the stability and safety of the overall structure. Currently, fastener inspection is primarily done manually, and there is no widely adopted automatic method for real-time detection of fastener looseness. We proposed a fastener looseness inspection method based on digital shearing speckle pattern interferometry (DSSPI) and convolutional neural network (CNN). The key distinction between our proposed method and traditional vision-based methods is that our approach can identify loose fasteners without relying on direct visual observation of the fasteners themselves. Our approach involved setting up a DSSPI system to capture shearing speckle patterns, and the CNN model automatically extracted features from these images and classified them to detect loose fasteners. Principle-validation experiments were conducted to verify the feasibility of the methodology. The trained model achieved a classification accuracy of 92.57%, with a resolution of 0.04 mm and a completion time of only 2.03 ms for a single judgment. That is quite accurate, more sensitive, and much faster than the three-dimensional vision-based method. Furthermore, our proposed method has strong robustness to ambient light, while the incorporation of Random Erasing technology enhances robustness to part occlusion or damage of the measured surface. Most notably, our method can detect loose fasteners before deformation, a capability that conventional vision-based methods lack. These findings demonstrate the tremendous potential of our method for real-time inspection of fastener looseness.

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“…The continuous wavelet coefficient [13] can be expressed as The velocity record is derived from the derivative of the displacement after Gaussian fitting. Therefore, the stress, strain, and strain rate of the specimen can be calculated according to the Formulae (12)- (14).…”

Section: Signal Processing Proceduresmentioning

confidence: 99%

“…where m is the scale parameter, n is the shift parameter, f (x) is the signal to be analyzed, ψ(x) is the mother wavelet, and ψ * (x − n)/m is its conjugate function. The amplitude of W f (m, n) is positively correlated with the similarity of the mother wavelet and the signal [14]. In this paper, Gaussian wavelet is chosen as the mother wavelet.…”

Section: Signal Processing Proceduresmentioning

confidence: 99%

“…According to Equations (12)- (14), the dynamic mechanical properties of the specimen depend on the velocity of the incident and transmitter bars, and the velocity is obtained by the displacement of the two bars after numerical differentiation, therefore, it is necessary to verify the displacement measurement capability of the constructed optical path. In other words, if the measurement accuracy of displacement can be guaranteed, then the dynamic mechanical properties of the sample can be considered accurate.…”

Section: Verification Experiments Of In-plane Displacement Measurementmentioning

confidence: 99%

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Non-Contact and Real-Time Measurement of Kolsky Bar with Temporal Speckle Interferometry

et al. 2018

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In this paper, a new non-contact and real-time measurement system for Kolsky bars is presented. This system uses two sets of temporal speckle interferometry in-plane displacement measurement devices to replace two strain gauges of conventional Kolsky bars. The in-plane displacement measurement of the Kolsky bar is mainly intended to provide a new test method for the dynamic mechanical properties of small-size material samples with diameters below 2 mm. This method is non-contact, does not require any intermediate medium, and can make the Kolsky bar applicable to characterizing the dynamic mechanical properties of materials under higher strain rates and smaller size conditions. The measuring devices and principles are described. In addition, a preliminary experiment is carried out to demonstrate the performance of this new device.

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Temporal heterodyne shearing speckle pattern interferometry

Cited by 6 publications

References 16 publications

Digital Shearing Speckle Pattern Interferometry Based on Rochon Prism and Its Application

Digital Shearing Speckle Pattern Interferometry Based on Rochon Prism and Its Application

Looseness inspection of fasteners based on digital shearing speckle pattern interferometry and convolutional neural network

Non-Contact and Real-Time Measurement of Kolsky Bar with Temporal Speckle Interferometry

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