Defect Detection In Composite Materials Research Articles

Defect detection of composite materials for cable insulation layer based on terahertz

Hidden defects can seriously affect the insulation performance of cables and pose safety hazards. In this article, a novel Non-Destructive evaluation technique for insulation materials based on terahertz time-domain spectroscopy technology is proposed. Firstly, terahertz time-domain spectroscopy technology was used to perform reflective scanning on insulation materials. Various imaging algorithms were used to image terahertz defects. The results showed that the maximum imaging and peak to peak time imaging were effective. At the same time, bilateral filtering was used to process terahertz defect images. The results showed that bilateral filtering not only filtered out noise in terahertz images, but also retained edge details. Terahertz time-domain spectroscopy technology offers a fresh approach for detecting cable insulation material defects non-destructively, and finds application in various electrical environment inspections.

Detection of flat-bottom holes in composite materials using multi-dimensional complementary ensemble empirical mode decomposition algorithm

Due to high-temperature resistance, high strength, and excellent fatigue resistance, composite materials are widely used in automotive manufacturing, aerospace, infrastructure and other fields. Consequently, the demand for defect detection of composite materials is also increasing. As a non-destructive testing technique, the active infrared thermography, which can achieve full-field defect detection, is suitable for defect detection of composite materials. However, this method is susceptible to noises caused by the environment and heating sources. In order to solve the problem of the defect signal being submerged by these noises, a multi-dimensional complementary ensemble empirical mode decomposition (MCEEMD) algorithm is introduced in this paper. This method can decompose the signal into the low-frequency background noise, the high-frequency heating noise, and useful defect signals, and these noises can be easily removed to improve the contrast to noise ratio (CNR) of defect images. Based on this proposed method, a defect detection experiment on the carbon fiber reinforced plastic (CFRP) is performed in this paper, and experimental results show that the method can effectively remove environmental noise and heating noise, and it can detect 11 out of 12 defects on the CFRP sample with an average CNR of 9.107. Compared with the traditional differential absolute contrast method, this method can detect one additional small defect with the aspect ratio of 1.67 and one deep defect with a depth of 2 mm.

Intelligent Detection Algorithm Based on 2D/3D-UNet for Internal Defects of Carbon Fiber Composites

ABSTRACT Infrared thermography testing (IRT) has been widely used in the defect detection of composite materials. However, the identification of defects characteristics is unsatisfying due to the interference of factors such as uneven background and noise in the original thermal image sequence. A novel thermography-based defect detection method with the semantic segmentation network is proposed to enhance the defect contrast and extract perfect features. To Figure out the abnormal distribution of temperature field in thermal images, AG-UNet was used with a spatial self-attention gate module to extract spatial features of thermal images. The 3D-UNet network was obtained by the 3D convolution module and the temporal convolution module to extract thermal temporal and spatial features simultaneously which could help the defect segmentation in thermal videos. Compared with traditional algorithms such as principal component analysis (PCA), thermographic signal reconstruction (TSR), and fast Fourier transform (FFT), defects detection results were significantly enhanced with the proposed method, and defects of smaller diameter-to-depth ratio can be detected by deep learning models.

Image Reconstruction of Planar Electrical Capacitance Tomography Based on DBSCAN and Self-Adaptive ADMM Algorithm

Planar electrical capacitance tomography (ECT) has broad application prospects in the defect detection of composite materials. The image reconstruction of planar ECT is a nonlinear and ill-posed inverse problem, which limits the accuracy of image reconstruction. An image reconstruction method based on the density-based spatial clustering of applications with noise cluster (DBSCAN) and self-adaptive alternating direction method of multipliers (SADMM) algorithms is proposed in this paper. Firstly, the characteristic capacitance values are extracted from the independent measurement capacitance values through the DBSCAN algorithm. Then, the original inverse problem is transformed into a set of local optimization sub-problems, and the SADMM algorithm with an adaptive penalty parameter is used to solve these subproblems to reconstruct the image. The experimental results show that the average relative error of the reconstructed images obtained by this algorithm is 0.086, which is significantly lower than other algorithms, the presented algorithm has higher anti-noise performance and can provide more accurate reconstructed images.

Defect Detection of Composite Material Terahertz Image Based on Faster Region-Convolutional Neural Networks

Terahertz (THz) nondestructive testing (NDT) technology has been increasingly applied to the internal defect detection of composite materials. However, the THz image is affected by background noise and power limitation, leading to poor THz image quality. The recognition rate based on traditional machine vision algorithms is not high. The above methods are usually unable to determine surface defects in a timely and accurate manner. In this paper, we propose a method to detect the internal defects of composite materials by using terahertz images based on a faster region-convolutional neural networks (faster R-CNNs) algorithm. Terahertz images showing internal defects in composite materials are first acquired by a terahertz time-domain spectroscopy system. Then the terahertz images are filtered, the blurred images are removed, and the remaining images are enhanced with data and annotated with image defects to create a dataset consistent with the internal defects of the material. On the basis of the above work, an improved faster R-CNN algorithm is proposed in this paper. The network can detect various defects in THz images by changing the backbone network, optimising the training parameters, and improving the prior box algorithm to improve the detection accuracy and efficiency of the network. By taking the commonly used composite sandwich structure as a representative, a sample with typical defects is designed, and the image data are obtained through the test. Comparing the proposed method with other existing network methods, the former proves to have the advantages of a short training time and high detection accuracy. The results show that the mean average precision (mAP) without data enhancement reached 95.50%, and the mAP with data enhancement reached 98.35% and exceeded the error rate of human eye detection (5%). Compared with the original faster R-CNN algorithm of 84.39% and 85.12%, the improvement is 11.11% and 10.23%, respectively, which demonstrates superb feature extraction capability and reduces the occurrence of network errors and omissions.

An optimization method of planar array capacitance imaging

Planar array capacitance imaging technology is a kind of nondestructive testing technology applied to defect detection of composite materials. In the imaging process, the image quality is often poor due to the environment noise and the ill-posed of inverse problem solving. In this paper, an image reconstruction optimization method is proposed. Based on the analysis of the sensitive region of a single pair of electrodes and the contribution of capacitance value to each solution unit, an optimization matrix which is calculated by adjusting the contribution coefficient of each capacitance value to different solution units is proposed to optimize the sensitive field. Finally, simulation and experiment results are presented to show the effectiveness of the proposed method. Through the average relative error of each pixel of the reconstructed image before and after optimization, it can be seen that the error of reconstructed images at two different positions are reduced by an average of 3.22 % and 1.62 % respectively, while verified this method can effectively improve the reconstructed image and improve the anti-noise ability of the image.

Analysis of Acoustic Signals for Diagnostics of Composite Materials

This article presents a method for the analysis of diagnostic acoustic signals for defect detection in composite materials. For this purpose, a multiple-scale decomposition of the acoustic signal and the determination of informational entropy for its components are used. It is established that, in the presence of small defects, the distribution of the power spectral density is little different from the power spectral density for a defect-free region. As a criterion for defect detection, a linear correlation coefficient is selected for vectors composed of informational entropies of multiple-scale components of the acoustic signal.

Time Segmented Image Fusion Based Multi- Depth Defects Imaging Method in Composites With Pulsed Terahertz

Recently, terahertz (THz) nondestructive testing technology has prevailed in the defect detection of composite materials yet is limited by identifying deeper flaws. To solve this problem, a time-domain piecewise imaging method based on terahertz signal extraction is proposed in this article. First, the surface reflection echo signal is removed by applying a time correction, and the time-domain signal is then segmented according to the thickness of the sample. Finally, high-quality terahertz images are obtained by image fusion technology. All imaging methods, including traditional ones, are utilized to detect defects in glass fiber reinforced plastic (GFRP) sample then the signal-to-noise ratio (SNR) and defect edge contrast of each method are calculated. The experimental results show that the proposed method can effectively improve the terahertz imaging of deeper defects in GFRP sample especially for multi-depth defects and possesses the highest SNR and defect edge contrast.

Comparative study of different infrared thermal wave imaging techniques for defect detection of composite materials

Comparative study of different infrared thermal wave imaging techniques for defect detection of composite materials

Ultrasonic Bulk Waves Measurements for Defect Detection of Composite Materials

The defect detection in composite material is important for its quality control where the hidden defect such as crack, corrosion, notch, holes, void and porosity can develop. In this paper, the ultrasonic bulk wave measurements of longitudinal and shear waves are used to identify defect in the multilayered composite material. This study employs pulse echo technique and utilized angle beam transducer. The composite material model investigated in this contribution are made of 24 mm and 12 mm thick Aluminium plates with a width of 100 mm and a length of 203 mm which are separated with an approximately 1 mm thick oil layer. A simulated defect is created in the composite test material by drilling a hole with 2.5 mm diameter and 3 mm depth on the bottom surface of the third layer material. Finding indicates that the defect is located at 53.39 mm from transducer and the percentage difference of the defect location compared to the calculation method is 7%. It indicates that the proposed method can be use to detect defect in multilayered composite material within 10% accuracy compared to the calculation method.

Ultrasonic field modeling in anisotropic materials by distributed point source method

DPSM (distributed point source method) is a modeling technique which is based on the concept of Green’s function. First, a collection of source and target points are distributed over the solution domain based on the problem description and solution requirements. Then, the effects from all source points are superimposed at the location of every individual target point. Therefore, a successful implementation of DPSM entails an effective evaluation of Green’s function between many pairs of source and target points. For homogeneous and isotropic media, the Green’s function is available as a closed-form analytical expression. But for anisotropic solids, the evaluation of Green’s function is more complicated and needs to be done numerically. Nevertheless, important applications such as defect detection in composite materials require anisotropic analysis. In this paper, the DPSM is used for ultrasonic field modeling in anisotropic materials. Considering the prohibitive computational cost of evaluating Green’s function numerically for a large number of points, a technique called “windowing” is suggested which employs the repetitive pattern of points in DPSM in order to considerably reduce the number of evaluations of Green’s function. In addition, different resolutions of numerical integration are used for computing Green’s function corresponding to different distances in order to achieve a good balance between time and accuracy. The developed anisotropic DPSM model equipped with windowing technique and multi-resolution numerical integration is then applied to the problem of ultrasonic wave modeling in a plate immersed in a fluid. The transducers are placed in the fluid on both sides of the plate. First an isotropic plate is considered for the sake of verification and rough calibration of numerical integration. Then a composite plate is considered to demonstrate applicability and effectiveness of the developed model for simulating ultrasonic wave propagation in anisotropic media.

A time-of-flight revising approach to improve the image quality of Lamb wave tomography for the detection of defects in composite panels

Abstract Lamb wave tomography can be used to evaluate structural integrity. The time-of-flight (TOF) data are usually recorded as input to the reconstruction algorithm. For composite materials, TOF estimation is complicated due to their anisotropy. To reduce the effects of anisotropy on image reconstruction, the TOF data of flawed plates are revised according to baseline data obtained from an unflawed plate. Tomographic images are reconstructed using the original and revised TOF data, respectively. Results show that images reconstructed using the revised TOF data have better visual quality and that TOF data revision can substantially reduce the artifacts resulting from anisotropy in defect detection of composite materials.

Prediction of blind frequency in lock-in thermography using electro-thermal model based numerical simulation

Lock-in thermography is increasingly becoming popular as a non-destructive testing technique for defect detection in composite materials for its low heating excitation. The experimental data is processed with Fourier transformation to produce phase and amplitude images. Phase images, though immune to surface emissivity variation, suffer from blind frequency effect, where a defect becomes invisible at a certain excitation frequency. There exists no analytical model to predict this 3-dimensional heat flow phenomenon. This paper presents a study of blind frequency using electro-thermal model based numerical simulation on a piece of thermally anisotropic carbon fibre composite. The performance of the simulator is optimized for spatial mesh size. Further the effect of paint layer, which is often applied to the sample surface for better thermal imaging, has been incorporated in the simulation. Finally, both experimental and simulation results are presented side-by-side for easy comparison.

Non-destructive Inspection of Multi-layered Composite Using Ultrasonic Signal Processing

Composites exhibit higher strength and stiffness, better design practice and greater corrosion resistance compare to metal material. However, composites are susceptible to impact damage and the typical damage behaviour in the laminated composites is fibre-breakage and delamination. Detection of failure in laminated composites is complicated compared with ordinary non-destructive testing for metal materials as they are sensitive to echoes drown in noise due to the properties of the constituent materials and the multi-layered structure of the composites. In the current study, the detection of failure in multi-layered composite materials is investigated. To obtain a high probability of defect detection in composite materials, signal processing algorithms were used to resolve echoes associated with defects in glass fibre-reinforced plastics (GRP) detected by using ultrasonic testing. Pulse-echo method with single transducer was used to transmit and receive ultrasound. The obtained signals were processed to reduce noise and to extract suitable features. Results were validated on GRP with and without defects in order to demonstrate the feasibility of the method on defect detection in composites.

Ultrasonic Inspection of Composite Materials Using Minimum Entropy Deconvolution

In this work, the Minimum Entropy Deconvolution (MED) method, developed for ultrasonic signals, is used to address the problem of delamination defect detection in Composite Materials. Standard deconvolution techniques suppose that the wavelet is minimum phase but generally make no assumptions about the amplitude distribution of the primary reflection coefficient sequence. For a white reflection sequence the assumption of a Gaussian distribution means that recovery of the true phase of the wavelet is impossible; however, a non-Gaussian distribution in theory allows recovery of the phase. It is generally recognized that primary reflection coefficients typically have a non-Gaussian amplitude distribution. The minimum entropy deconvolution (MED) method supposes whiteness but seek to exploit the non-Gaussianity. This method do not assume minimum phase. The deconvolution filter is defined by the maximization of a function called the objective. The algorithm is tested on simulated data and also tested on real ultrasonic data from multilayered composite materials.

Automatic ultrasonic inspection for internal defect detection in composite materials

The detection of internal defects in composite materials with non-destructive techniques is an important requirement both for quality checks during the production phase and in-service inspection during maintenance operations. Visual inspection allows only the analysis of surface characteristics of materials and, then, if internal faults occur inside composite structures, a deeper analysis is required. A comparison between the reactions of different materials to ultrasonic signals can be used to highlight the difference in the internal structures and also to detect the depth position of these anomalies. However, ultrasonic data are difficult to interpret since they require the analysis of a continuous signal for each point of the material under consideration. An automatic procedure is necessary to manage large data sets and to extract significant differences between them. In this paper, we address the problem of automatic inspection of composite materials using an ultrasonic technique. We consider two main steps for interpreting ultrasonic data: the pre-processing technique necessary to normalize the signals of composite structures with different thicknesses and the classification techniques used to compare the ultrasonic signals and detect classes of similar points.

Research of probability characteristics in defect detection of composite materials using wavelet transform

Parameters of identification of surface crack signals Kh and Kw are proposed based on features of the Fourier spectrum and the wavelet spectrogram of modulation impulses defined using an eddy current sensor. Probability performances of defect detection using both spectral and wavelet methods are defined depending on the probability of a false alarm. Simulation data revealed that both methods have a good ability to identify short and shallow surface cracks. The wavelet method has the best probability of defect detection compared with the spectral method in case intensive noise. The spectral method is faster and preferable at small noise levels.