Showing papers on "Ultrasonic testing published in 2022"
TL;DR: A comprehensive overview of various non-destructive testing (NDT) techniques for wire and arc additive manufacturing (WAAM) and fusion welding can be found in this paper , where the minimum defect size that can be identified via NDT methods has been obtained from previous academic research or from tests carried out by companies.
Abstract: In Wire and Arc Additive Manufacturing (WAAM) and fusion welding, various defects such as porosity, cracks, deformation and lack of fusion can occur during the fabrication process. These have a strong impact on the mechanical properties and can also lead to failure of the manufactured parts during service. These defects can be recognized using non-destructive testing (NDT) methods so that the examined workpiece is not harmed. This paper provides a comprehensive overview of various NDT techniques for WAAM and fusion welding, including laser-ultrasonic, acoustic emission with an airborne optical microphone, optical emission spectroscopy, laser-induced breakdown spectroscopy, laser opto-ultrasonic dual detection, thermography and also in-process defect detection via weld current monitoring with an oscilloscope. In addition, the novel research conducted, its operating principle and the equipment required to perform these techniques are presented. The minimum defect size that can be identified via NDT methods has been obtained from previous academic research or from tests carried out by companies. The use of these techniques in WAAM and fusion welding applications makes it possible to detect defects and to take a step towards the production of high-quality final components.
17 citations
TL;DR: In this paper , the authors evaluated multiple state-of-the-art deep learning anomaly detection methods on the ultrasonic non-destructive testing dataset and achieved an average performance of almost 82% of ROC AUC.
12 citations
TL;DR: In this paper, an intelligent denoise laser ultrasonic imaging method was developed to inspect the micro defects on the rough surface of SLM components, which can increase the average SNR from 27.0 dB to 35.2 dB.
Abstract: The random microdefects are inevitable during the Selective laser melting (SLM) process due to the principle of discrete-stacking. The rough surface induced strong background noise reduces the probability of detection of traditional laser ultrasonic testing system. In this study, an intelligent denoise laser ultrasonic imaging method was developed to inspect the micro defects on the rough surface of SLM components: (1) a non-contact laser ultrasonic scanning setup was established for data acquisition of multiple ultrasonic signals; (2) a denoising algorithm based on unsupervised machine learning was designed and trained by abundant ultrasonic data to enhance the signal to noise ratio (SNR); (3) a signal matching algorithm based on the cross-correlation and self-normalized method was established to match the amplitude and arrival time of Rayleigh waves from different scanning points. The performance of developed method was verified using micro hole defects on the rough surface of a SLM part. The results indicated that the established denoising algorithm could increase the average SNR from 27.0 dB to 35.2 dB. All holes with diameter of 50 μm and 100 μm can be detected and sized based on the high SNR image without removing the rough surface. The conclusion can be drawn that the proposed intelligent denoise laser ultrasonic imaging method is a very potential way for the online inspection of SLM.
10 citations
TL;DR: In this article , the authors give an overview of signal processing techniques used for filtering signals, isolating modes and identifying and localising defects in UGWT, summarised and grouped according to the geometry of the studied structures.
Abstract: Ultrasonic guided wave testing (UGWT) is a non-destructive testing (NDT) technique commonly used in structural health monitoring to perform wide-range inspection from a single point, thus reducing the time and effort required for NDT. However, the multi-modal and dispersive nature of guided waves makes the extraction of essential information that leads to defect detection an extremely challenging task. The purpose of this article is to give an overview of signal processing techniques used for filtering signals, isolating modes and identifying and localising defects in UGWT. The techniques are summarised and grouped according to the geometry of the studied structures. Although the reviewed techniques have led to satisfactory results, the identification of defects through signal processing remains challenging with space for improvement, particularly by combining signal processing techniques and integrating machine learning algorithms.
10 citations
TL;DR: In this paper , a convolutional neural network model was established to recognize wrinkles at different depths with the short-time Fourier transform of waveforms, and the model training could be accelerated through simulation in PZFlex.
10 citations
TL;DR: In this paper , a new technique of creating a delamination defect is presented to produce a thin air gap for measuring realistic ultrasonic responses, and a support vector machine (SVM) classifier has been used to improve delamination detection accuracy.
10 citations
TL;DR: In this paper, the authors identified fatigue cracks in a steel joint under vibration by nonlinear guided waves based on second harmonic generation, where piezoelectric transducers were used to excite S1 Lamb wave mode in experiment and the nonlinear ultrasonic testing was performed at different crack lengths.
10 citations
TL;DR: In this article , the authors investigated the influence of surface profile on the detection of wire arc additive manufacturing (WAAM) defects using the multiscale analysis of the sample surface profile.
9 citations
TL;DR: In this paper, the authors proposed the use of Generative Adversarial Networks for generating realistic ultrasonic images, which is the first one to show that a GAN is able to generate images indistinguishable from real ultrasonic image.
9 citations
TL;DR: In this paper, an ultrasound generation and sensing system using a femtosecond laser is developed specifically for noncontact inspection of a moving object, which is used to generate and measure ultrasound from a silicon wafer while the wafer is moving in a horizontal direction.
8 citations
TL;DR: In this paper , a self-interference cancellation algorithm based on the matching pursuit algorithm is proposed, which can eliminate the waveform generated by multiple reflections in the echoed signal, and the calculated damage location is compared with the model's actual parameters, which shows that the algorithm can be applied to the defect location of multilayer composites.
TL;DR: In this article , a high sensitivity technique for detecting creep damage (Type IV cracking) based on focused ultrasound was implemented. But it was only applied on low-alloy ferritic steel pipes and headers in power stations.
TL;DR: In this paper, a self-interference cancellation algorithm based on the matching pursuit algorithm is proposed, which can eliminate the waveform generated by multiple reflections in the echoed signal, and the calculated damage location is compared with the model's actual parameters, which shows that the algorithm can be applied to the defect location of multilayer composites.
TL;DR: In this article , the authors present suitable approaches for solving the ray-tracing problem in multi-layered structures, using the Newton-Raphson root-finding method with bisection-type iterations.
TL;DR: In this paper , the results of ultrasonic non-destructive testing of carbon fiber-reinforced plastics (CFRP) and glass-fibre reinforced plastics (GFRPs) were presented.
Abstract: This paper presents results of ultrasonic non-destructive testing of carbon fibre-reinforced plastics (CFRPs) and glass-fibre reinforced plastics (GFRPs). First, ultrasonic C-scan analysis was used to detect real defects inside the composite materials. Next, the composite materials were subjected to drilling in the area of defect formation, and measured forces were used to analyse the drilling process using recurrence methods. Results have confirmed that recurrence methods can be used to detect defects formed inside a composite material during machining.
TL;DR: In this article, the authors describe the first study to perform ultrasonic guided wave measurements in a wooden rod using arrays of shear transducers, and the results indicate that ultrasonic-guided wave testing techniques have strong potential to be used to provide improved measurement of wood properties and structural health monitoring of wooden structures.
TL;DR: In this article , the authors proposed a module composition and positioning principle for an ultrasonic transducer based on an ULISSD for calculating the reflection distance, which is the primary performance index of a logging system.
Abstract: High-precision logging equipment is critical for measuring the borehole diameter and drilling offset in coal mining and petroleum drilling. We propose a module composition and positioning principle for an ultrasonic transducer based on an ultrasonic logging instrument for shaft sinking by drilling (ULISSD) for calculating the reflection distance. The logging distance, which is the primary performance index of a logging system, is determined by using the self-reception sensitivity and error of the ultrasonic transducer in a downhole system. To measure the error between the piezoelectric element of the transducer and the rubber seal of the borehole logging system, we developed an ultrasonic-transducer error-calibration device and a calibration method for a central-air-return-shaft-drilling project. This calibration device can eliminate the inherent error of the transducer and calculate the rate of propagation with high accuracy. The measurement error is reduced by approximately 1.5 mm; thus, the ULISSD measurement accuracy can be effectively improved in central-air-return-shaft drilling.
TL;DR: In this paper, an adaptive autoencoder was proposed to predict the normal behavior of ultrasonic signals including disturbances, thus enabling the identification of even subtle deviations made by defects.
TL;DR: In this article , an intuitive model was developed to explore the physical mechanism of a micro-defect-induced variation of generated second harmonic of a primary Lamb wave in a plate.
Abstract: Ultrasonic inspection is a widely used nondestructive testing approach in industrial fields for more accurate life prediction and efficient management strategies of critical structural components. However, it is quite challenging to detect a kind of micro-defect, whose size is much smaller than the ultrasonic wavelength but larger than the ultrasonic amplitude (namely, there is no appearance of non-classical acoustic nonlinearity). In this article, identification and imaging of a micro-defect of this size range with an improved resolution is conducted by the combination of the second-harmonic generation (SHG) of ultrasonic Lamb waves and the reconstruction algorithm for the probability inspection of damage. An intuitive model is first developed to explore the physical mechanism of a micro-defect-induced variation of generated second harmonic of a primary Lamb wave in a plate. Variations of amplitudes of second harmonics generated in propagation paths are used to construct the micro-defect image. A phase-reversal technique is employed to enhance the signal-to-noise ratio of the SHG. Comparisons between images constructed by linear and nonlinear acoustic features of Lamb wave propagation are presented. Results show that the image of the micro-defect with an improved resolution is successfully obtained by the proposed approach, while there is no visualized result obtained by the conventional linear ultrasonic one.
TL;DR: In this paper , a nonlinear Lamb wave imaging method for detecting the Barely Visible Impact Damage (BVID) of Carbon Fiber Reinforced Polymer (CFRP) laminates was proposed.
TL;DR: In this paper , a mathematical tool capable of mitigating positioning errors through the correction of focal laws is presented, and the relative firing delays of the active elements are calculated considering the temperature gradients along the sonic path, and results demonstrate a reduction of more than 68% in the error of flaw positioning.
Abstract: Ultrasonic inspection techniques and non-destructive tests are widely applied in evaluating products and equipment in the oil, petrochemical, steel, naval, and energy industries. These methods are well established and efficient for inspection procedures at room temperature. However, errors can be observed in the positioning and sizing of the flaws when such techniques are used during inspection procedures under high working temperatures. In such situations, the temperature gradients generate acoustic anisotropy and consequently distortion of the ultrasonic beams. Failure to consider such distortions in ultrasonic signals can result, in extreme situations, in mistaken decision making by inspectors and professionals responsible for guaranteeing product quality or the integrity of the evaluated equipment. In this scenario, this work presents a mathematical tool capable of mitigating positioning errors through the correction of focal laws. For the development of the tool, ray tracing concepts are used, as well as a model of heat propagation in solids and an experimentally defined linear approximation of dependence between sound speed and temperature. Using the focal law correction tool, the relative firing delays of the active elements are calculated considering the temperature gradients along the sonic path, and the results demonstrate a reduction of more than 68% in the error of flaw positioning.
TL;DR: In this paper , massive data for a frequency effect with comparisons of the data between ultrasonic and conventional fatigue testings are exampled on low-and high-strength steels, austenitic stainless steels and cast irons.
Abstract: The ultrasonic fatigue testing method was standardized in the Japan Welding Engineering Society. Scope of this standard is limited for metallic materials tested at room temperature. This standard applies not only to axial loading by longitudinal oscillation but also to torsional loading by torsional oscillation. The stipulated specimens are an hourglass-shaped type, while other types of specimens are also acceptable. Explanation attached to this standard discloses data and theories behind each requirement. In particular, massive data are disclosed for a frequency effect with comparisons of the data between ultrasonic and conventional fatigue testings. The data are disclosed on low- and high-strength steels, austenitic stainless steels, cast irons, titanium alloys, and aluminum alloys. These data suggest that the ultrasonic fatigue testing is well suited to high-strength materials but not low-strength materials. In addition to the introduction of the standard, studies using the ultrasonic fatigue testing are exampled in this paper.
TL;DR: In this article , an imaging optimization algorithm based on least-mean-square (LMS) adaptive filtering and angle gain compensation (AGC) is proposed to inspect the aeroengine casing ring forgings.
TL;DR: In this article , a deep learning neural network is used to classify the characteristic acoustic emission waveforms propagated through the CORTEN steel and attenuated due to the geometrical configuration and corrosion formation.
Abstract: The integrity of the CORTEN steel exposed to corrosion agents is studied using Acousto-Ultrasonic approach. The Acousto-Ultrasonic approach was tested before and after exposing the CORTEN steel to the corrosion agent. The waveforms recorded from the Acousto-Ultrasonic tests are analysed using Mel Spectrogram. The attenuation in the wave propagation due to the extent of corrosion and due to the geometrical configuration of the CORTEN steel test specimens is studied in time-frequency domain. The Mel scale is used for analysing the time-frequency characteristics of the recorded waveforms. A Deep Learning Neural Network is constructed for analysing the waveforms recorded from the Acousto-Ultrasonic test. Deep learning is used to classify the characteristic acoustic emission waveforms propagated through the CORTEN steel and attenuated due to the geometrical configuration and corrosion formation. The Convolutional Neural Network (CNN) is built in MATLAB® and is trained to classify the acoustic emission waveforms recorded from the Acousto-Ultrasonic test.
TL;DR: In this paper, the impact of the transducer size and center frequency on the accuracy of bolt fastening force measurement has been investigated and the results show that when the nominal diameter of a bolt is close to the diameter of the wafer of the ultrasonic transducers, the bolt is the highest.
TL;DR: In this paper, the authors examined the fatigue-crack closure induced by heat processing using a surface-acoustic-wave phased array (SAW PA), which is a phased array imaging method using Rayleigh waves, which can sensitively visualize defects in the vicinity of surfaces.
TL;DR: In this article , the most relevant published work on different types of techniques for non-destructive evaluation (NDE) of various defects using Ultrasonic is brought together, along with motions to assess the performance of the techniques.
TL;DR: In this article , the quantitative relationship between the introduced hydrogen content and the mechanical property degradation of 2.25Cr-1Mo-0.25V steel was investigated using electrochemical hydrogen charging technique and tensile testing, and a nondestructive testing method based on pulse-echo ultrasonic measurement technique, combined with the results of mechanical assessment for hydrogen embrittlement (HE) susceptibility, was proposed to indirectly estimate the degree of hydrogen-induced plasticity loss of the steel.