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In the intelligent production process of wood products, the classification system of wood knot defects is a very practical solution. However, traditional image processing methods cannot handle it well due to the uncertainty of manually extracted features. Therefore, a lightweight and reliable artificial neural network model is proposed to classify and identify our objective. To solve this problem, a wood knot defect recognition model named SE-ResNet18 combining convolutional neural network, attention mechanism, and transfer learning is proposed in this paper. First, the Sequence-and-Exception (SE) module is combined with Basicblock and is constructed as two modules called RBBSE-1 and RBBSE-2. These modules learn to enhance features that are useful for the current task, suppress useless features, and fuse the output features with the original features. Then, the fully connected layer is replaced with a global average pooling layer, which can effectively reduce the parameters of the fully connected layer in the model. Finally, a SE-ResNet18 was constructed by one convolutional layer, five RBBSE-1 modules, and three RBBSE-2 modules of different channels. The SE-ResNet18 has a higher accuracy (98.85%) in the test set compared to the unimproved model ResNet-18. Compared with the previously proposed ReSENet-18, more SE modules are used in SE-ResNet18 to provide a basis for future training on a larger-scale dataset. Based on the same test set, a comparison with other classical models (such as LeNet-5, AlexNet, etc.) was conducted, and the results validated the superiority of the proposed model. The proposed model achieves the expected objective and provides a new way of thinking for non-destructive testing of wood. [ABSTRACT FROM AUTHOR]

Defects, such as delamination and debonding, are critical to the performance of carbon fiber-reinforced polymer (CFRP) composites. In recent years, non-destructive testing techniques have been improved for the inspection of these defects among CFRPs. In this study, an all-optical and non-destructive laser ultrasonic technique with an optical microphone detection module has been presented to detect the artificial subsurface defects among the CFRP composites. A finite element simulation based on the thermo-mechanical coupling model was used to study the process of nanosecond pulsed laser excitation of the CFRP laminate to produce ultrasound and the propagation behavior of ultrasound among the CFRP laminate. A series of non-contact laser ultrasonic testing experiments were carried out to study the flat bottom holes of different sizes via a laser ultrasonic detection system. The artificial subsurface defects were reliably identified by the presented all-optical laser ultrasonic system imbedded in the optical microphone using four feature images. [ABSTRACT FROM AUTHOR]

In this article, a low-energy chirp-pulsed radar thermography (CP-RT) is used to detect the subsurface delamination of carbon fiber reinforced polymer (CFRP) composite as nondestructive testing and evaluation (NDT&E) techniques. The CFRP specimen with artificial flat-bottom holes (FBHs) is prepared for NDTandE by chirp-pulsed radar thermography. Two lasers are employed to be external excitation heat sources. The laser intensities are modulated according to a chirp-pulsed radar signal that combines linear frequency modulation and pulse excitation, and the temperature rise is controlled within 2 °C in the experiment. The thermal-wave response signal is processed by a series of different postprocessing characteristic extraction algorithms. These algorithms include time–frequency algorithms [crosscorrelation algorithm (CC), fast Fourier transform (FFT), and dual-orthogonal demodulation algorithm (DOD)] and statistical analysis approaches [principal component analysis (PCA) and PCA-based reconstructed independent component analysis (PCA-RICA)]. The signal-to-noise ratio (SNR) of defects is employed to evaluate the defect detectability for different size defects by different postprocessing algorithms. A three-dimensional (3-D) tomography method based on the FFT phase characteristic is proposed. A truncated-correlation photothermal tomography based on DOD is also introduced to enable the 3-D tomography of CFRP specimen. The FFT phase presents a relatively high SNR and has good correlation with the depth of the defect. The FFT-based CP-RT has the potential to provide a rapid NDT&E and 3-D tomography approach for CFRP with subsurface defects under the low-energy excitation condition. [ABSTRACT FROM AUTHOR]

Precise and efficient inspection of disband defect in sub-mm layers of composite structures is a big challenge for the non-destructive and testing industry. In this work, 16 artificial disbands (flat bottom holes), with different sizes and depths, in carbon fiber reinforced polymer (CFRP) composites were contactless detected using combined laser thermography and laser ultrasonics with optical microphone. The presented laser ultrasonic technique employed a membrane free optical microphone is a contactless and nondestructive technique and shows high detectability for flat bottom holes with deep depth in this preliminary investigation. Sizes and depths of the flat-bottom holes were determined by the presented laser ultrasonics technique. This investigation combined the high detection efficiency of laser thermography and high detection sensitivity of laser ultrasonics with the optical microphone together for the contactless inspection of artificial disbonds. [ABSTRACT FROM AUTHOR]

Abstract: Lock-in thermography, an active IR thermography technique for NDT, is based on propagation and reflection of thermal waves which are launched from the surface into the inspected component by absorption of modulated radiation. In this paper, thermal wave image sequences were sampled by a Cedip JADE MWIR 550 FPA infrared camera. Thermal wave signal processing algorithms are investigated to obtain information on subsurface defects. The Fourier transform, four-point correlation, and digital lock-in correlation algorithms are applied to extract the amplitude and phase of thermal wave’s harmonic component. A novel method called the time constant method (TCM) is proposed to analyze subsurface defects by using lock-in thermography. The experimental results confirm the thermal wave signal processing algorithms’ efficiency on subsurface defect detection. [Copyright &y& Elsevier]

• CP-PRI is used to recognize the surface crack at GFRP composite materials. • The effects of the modulation excitation parameters of CP-PRI is analyzed systematically. • CP-PRI can recognize small surface cracks (~150 μm). Laser excitation chirp pulsed photothermal radar imaging (CP-PRI), as a novel nondestructive testing and evaluation (NDT&E) approach, is employed to recognize the surface crack at glass fiber reinforced polymer (GFRP) composite materials. The mechanism of crack detection has been demonstrated in detail by a photothermal method and the principle of CP-PRI. A GFRP specimen with artificial surface crack is prepared for NDT&E by CP-PRI. First-order differential processing was employed to enhance edge sharpening of the amplitude image. The effects of the laser power, excitation pulsed width, reference pulsed width, delay time and chirp frequency on the signal-noise-ratio (SNR) of cracks are completely investigated. The results illustrated that the SNR of the crack is increased with the increase of laser power, the opening width of crack and excitation pulse width. The reference pulse width has few implications for the SNRs of the smaller opening width cracks (~100 μm). However, the reference initial delay time and chirp frequency had a significant influence on the SNRs of cracks. The comparison experiments between thermal wave radar imaging (TWRI) and CP-PRI has been carried out, its results indicated that the CP-PRI has the higher SNRs of cracks than that of TWRI. Consequently, CP-PRI is available for the detection of GFRP surface cracks as a result of its high detectability. [ABSTRACT FROM AUTHOR]

In this paper, a Barker-coded thermal wave imaging approach is reported on the detection of carbon fiber reinforced polymer (CFRP) laminate with subsurface defects, using an integrated Barker code sequence and sinusoidal carrier-modulated laser as the excitation source. Artificial flat bottom holes as subsurface defects are prepared for the experimental investigation. Cross-correlation (CC) algorithm is applied for extracting characteristics of thermal wave signal and forming the corresponding peak delay time and phase images. The effects of Barker code sequence length and carrier-modulated frequency are investigated, which are both most important factors on the detectability of BC-TWI method. The results of the experiments show 5-bit Barker code and 0.1 Hz carrier frequency are the most suitable selection for enhancing inspection capability and obtaining the highest image SNR for a given CFRP laminate material. Furthermore, a comparative experiment is carried out between BC-TWI and lock-in thermography (LIT) method by taking the defect contrast and SNR into account. The results indicate that the BC-TWI CC phase image has higher contrast and SNR than the LIT phase image. [ABSTRACT FROM AUTHOR]

In this paper, thermal-wave radar imaging (TWRI) is introduced to detect debonding defects in SiC-coated Ni-based superalloy plates. Linear frequency modulation signal (chirp) is used as the excitation signal which has a large time-bandwidth product. Artificial debonding defects in SiC coating are excited by the laser beam with the light intensity modulated by a chirp signal. Cross-correlation algorithm and chirp lock-in algorithm are introduced to extract the thermal-wave signal characteristic. The comparative experiment between TWRI reflection mode and transmission mode was carried out. Experiments are conducted to investigate the influence of laser power density, chirp period, and excitation frequency. Experimental results illustrate that chirp lock-in phase has a better detection capability than other characteristic parameters. TWRI can effectively detect simulated debonding defects of SiC-coated Ni-based superalloy plates. [ABSTRACT FROM AUTHOR]

• A three-layer model was developed to study the electric field distributions. • A 3D microwave model for the two-layer TPS containing the specified defects was established using CST to intuitively present the influences of flaws on the distribution of reflected coefficients. • The disbond defect between the two-layer TPS could be clearly identified by the phase information after correlation processing algorithms. Reusable thermal protective system (TPS) plays a key role for protecting space vehicles against the aerothermal heating. Thus the characterization of TPS performance becomes an important issue in their production, processing and applications. This paper demonstrates the detection and imaging of inner disbond and thick defect, which considerably compromises the performance of TPS materials within a two-layer TPS configuration (a special designed thick heat insulation tile backed by an aluminum alloy plate). The methodology employed for this investigation is a near-field microwave non-destructive testing (NDT), encompassing both simulations and experimental study. A 3D microwave model for the two-layer TPS structure containing the specified defects was established, and the magnitude and phase of the waveguide input impedance (i.e. S 11 parameters) for each node within the X-band (8.2 ∼ 12.4 GHz) frequency range were obtained in the work. The simulation results reveal distinct featured amplitude and phase images of defects at various frequencies. Correlation processing algorithms were employed to make the images dimensionally reduction and featured enhancement. Finally, the results were verified by experimental measurements using an open-ended rectangular waveguide. Interestingly, the disbond defect between the two layers could be clearly identified by the phase information after correlation processing algorithms. The proposed method appears to offer a contactless, intuitive, and effective approach for characterizing defects within TPS materials. [ABSTRACT FROM AUTHOR]

• The principle of multimode infrared thermal-wave imaging is briefly introduced initially. • Several typical infrared thermal-wave imaging modalities are introduced in detail from the aspects of the excitation source, excitation waveform, feature extraction algorithm, and image processing. • The development status and practical application examples of multimode infrared thermal-wave imaging are emphatically introduced, and the problems faced by infrared thermal-wave imaging are analyzed. Multimode infrared thermal-wave imaging has the advantages of intuitive, efficient, large detection area, non-contact, and long-distance detection respectively. It has a wide range of applications in the aerospace, nuclear industry, petrochemical, automobile, and energy fields. In this present investigation, the principle of multimode infrared thermal-wave imaging is briefly introduced initially. Subsequently, several typical infrared thermal-wave imaging modalities, such as pulse thermography, lock-in thermography, line scanning infrared thermography, and infrared radar thermal-wave imaging, are introduced in detail from the aspects of the excitation source, excitation waveform, feature extraction algorithm, and image processing. Multimode infrared thermal-wave imaging based on different heat source triggering modes was also introduced in this paper. The development status and practical application examples of multimode infrared thermal-wave imaging are emphatically introduced, and the problems faced by infrared thermal-wave imaging are analyzed. Finally, the future development trend of multimode infrared thermal-wave imaging is prospected. [ABSTRACT FROM AUTHOR]