Aiming at the drawbacks of low contrast and high noise in the thermal images, a novel method based on the combination of the thermal image sequence reconstruction and the first-order differential processing is proposed in this work, which is comprised of the following procedures. Firstly, the specimen with four fabricated defects with different sizes is detected by using pulsed infrared thermography. Then, a piecewise fitting based method is proposed to reconstruct the thermal image sequence to compress the data and remove the temporal noise of each pixel in the thermal image. Finally, the first-order differential processing based method is proposed to enhance the contrast. An experimental investigation into the specimen containing de-bond defects between the steel and the heat insulation layer is carried out to validate the effectiveness of the proposed method via the above procedures. The obtained results show that the proposed method can remove the noise, enhance the contrast, and even compress the data reaching at 99.1%, thus improving the detectability of pulsed infrared thermography on metal defects.
Nonlinear vibration plays a vital role in ultrasonic infrared thermography testing due to contact-collision effect between a horn and sample, and the effects of contact roughness and sample mass on vibration characteristic of a horn-sample system are deeply investigated in this paper. A two-degree-offreedom dynamic model based on GW contact model is proposed for exploring the vibration characteristic of a horn-sample system. Then, the vibration characteristics of the horn and sample are calculated under different contact roughness and different sample mass. The results demonstrate that the contact roughness does not influence vibration characteristic of horn, but there are mainly 2f and 3f except the first harmonic f in velocity responses of the sample. Besides, the amplitude of sample velocity increases with the increase of real contact area as the contact interface is smoother, and there is a 1.8-fold approximate relation in the three amplitude of harmonic f under different contact roughness. On the other hand, with the increase of sample mass, the harmonic f becomes the main component and higher harmonics decreases little by little. This work will be extraordinarily meaningful to optimize testing parameters and evaluate surface quality of sample in ultrasonic infrared thermography testing.
This paper proposes a new algorithm to generate representative volume elements (RVEs) with random fiber distribution in fiber reinforced composites (FRC). The proposed algorithm is straightforward and easy to implement based on judging the maximum and minimum distances between a new fiber and existing fibers. The generation results demonstrate that the maximum fiber volume fraction gradually increases and oscillates violently before reaching 78.54% as the fiber radius rises. Moreover, with the increase of RVE size, the maximum fiber volume fraction changes gently when the fiber radius does not exceed 6.5 μm, but it changes dramatically at other fiber radii. Then, the fiber distributions of the generated RVEs are evaluated using the nearest neighbor distance, Ripley’s K function, and pair distribution function. The evaluation results indicate that the fiber distributions present randomness. Lastly, the effective elastic properties of the Carbon/Epoxy unidirectional FRC are predicted using the RVEs generated by the proposed algorithm, the RVEs generated by regularization, and the Mori–Tanaka method. It is found that the prediction using the RVEs generated by the proposed algorithm is more accurate than the regularization, compared with the Mori–Tanaka and experiment results. The proposed algorithm contributes to microstructure modeling in computational micromechanics.
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