This paper introduces a technique to identify defects from fringe patterns for optical non-destructive testing and metrology. The technique relies on computation of the windowed Fourier spectrum of the fringe pattern at a given spatial frequency, and subsequent application of automated spectrum thresholding to localize the defect. The technique offers the advantages of high robustness against noise, fast implementation, high throughput and minimal operator intervention. The performance of the proposed technique is demonstrated for identifying defects of different types and sizes under varying levels of noise using numerical simulations, and practical validity is tested using experimental interferograms obtained in diffraction phase microscopy.
The paper introduces a method for studying flow dynamics using diffractive optical element based background-oriented schlieren (BOS). Our method relies on fringe demodulation using root multiple signal classification technique which provides high robustness against noise. Further, a graphics processing unit (GPU) based implementation is proposed which offers significant improvement in computational efficiency, and thus enables high speed analysis of flows. The performance of the method is demonstrated via numerical simulations and the practical applicability is also shown by analyzing a diffusion phenomenon in liquids by BOS.
The paper presents a method for automated defect identification from fringe patterns. The method relies on computing the fringe signal’s Wigner–Ville distribution followed by a supervised machine learning algorithm. Our machine learning approach enables robust detection of fringe pattern defects of varied shapes and alleviates the limitations associated with thresholding-based techniques that require careful control of the threshold parameter. The potential of the proposed method is demonstrated via numerical simulations to identify different types of defect patterns at various noise levels. In addition, the practical applicability of the method is validated by experimental results.
Dynamic measurement of surface profile is an important requirement in nondestructive testing, especially for the inspection of large samples with consecutive area scans or test objects under translation. In this paper, we propose the application of an eigenspace signal analysis method in diffraction phase microscopy for reliable and noncontact dynamic surface metrology. We also propose the inclusion of a graphics processing unit (GPU) computing framework in our method to enable fast interferogram processing for dynamics-based investigations. The practical viability of the proposed method is demonstrated for noninvasive nanoscale topography of a test target.
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