In this paper, a phase denoised digital speckle pattern interferometry (DSPI) method based on improved variational mode decomposition (IVMD) is proposed to reduce the noise interference in DSPI phase maps. The first step is to decompose the DSPI phase map using the IVMD method and obtain the optimal mode components. Then, an adaptively mode threshold method is proposed for processing the mode components based on the characteristics of the mode components. To obtain the denoised DSPI phase map, the denoised mode components are reconstructed. The results demonstrate that the proposed method is effective in filtering out noise interference and the peak signal-to-noise ratio (PSNR) is higher than that of other denoised methods.
As a high-precision detection technique of surface topography based on the interference theory of light, Phase-Shifting Interferometric is susceptible to vibration from the surrounding environment, which reduces the detection accuracy. In order to reduce the influence of vibration errors and improve the phase demodulation accuracy of measured wavefront, an anti-vibration phase demodulation method applied to Phase-Shifting Interferometric is proposed in this paper. This method first acquires two sequences of interference images with phase shifts of 0 and 𝜋𝜋/2 respectively, uses a digital image stabilization algorithm to suppress the vibration error of each frame in the sequence, and synthesizes two frames of interference images with phase shifts of 0 and 𝜋𝜋/2, respectively. Then, based on these two frames of synthesized images with determined phase shifts, the gradient algorithm and the Schmidt Orthogonal Transform are used for wavefront phase demodulation. The experimental results show that the percentage of pixels demodulated effectively at the truncated interface is more than 90%. Compared with the traditional demodulation method, the number of pixels demodulated effectively (demodulation accuracy) is improved.
The wavelength tuning interferometer is widely used in the detection of large-aperture optical components. Limited by the accuracy of wavelength modulation, the calculation of phase shift is difficult to meet the accuracy requirements. At present, the least squares iterative algorithm is often used to solve the phase. However, this algorithm needs to accurately calibrate the cavity length first, and the calculation error of the phase shift amount in some specific intervals is large, especially when there is a contrast difference between the interferograms, the detection accuracy is even more difficult to meet the requirements. This paper proposes a method to analyze the phase shift directly from the interferogram, using an improved iterative algorithm to calculate the wave-front phase, and proposes a self-applicable segmentation processing method for the phase calculation in a specific interval to meet the system accuracy and robustness requirements. The validity of the proposed method is verified by the modeling method, and the existing wavelength-tunable laser interferometer is used to test the sample. The repeatability of PV value and RMS value are λ⁄50 and λ⁄100 respectively.
High-power fiber laser welding has proven to be the most effective high-speed automatic welding technology. It is generally believed that the keyhole structure contains a large amount of welding quality information. Its behavior and instability are one of the causes of welding quality defects, especially the porosity. In this paper, we propose a fast-online detection method for high-power fiber laser welding. In view of the characteristics that the behavior and stability of the keyhole have an important impact on the welding quality, the real-time image of the keyhole is taken during the welding process, and the image is binarized through the adaptive threshold selection, and the maximum connecting area is selected to quickly get the contour of the keyhole. By combining the global convexity of the keyhole contour with the local angularity on the micro level, the support vector machine model is trained as the input data. Experiments show that the classifier has high accuracy. The combination of these features can characterize the pore defects, quickly and real-time find potential pores, reduce the cost and time of later detection, and explain abnormal metal flows in and out of keyholes when defects occur.
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