Flaw Detection Research Articles

Thermal Flaw Detection Scanner for Testing Large-Sized Flat Products Made of Composite Materials

A self-propelled thermal flaw detector has been developed for testing large-sized flat parts by means of thermal scanning that provides higher efficiency and test performance than conventional thermal testing scheme over individual zones. The flaw detector is designed to detect delaminations, impact damage, and foreign inclusions in composite materials and can be used to test for corrosion in metal shells, providing continuous monitoring with a performance of up to 20 m2/h.

Elastic Guided Waves in Bistable Composite Structures - Experimental and Numerical Investigation

Background: Bistable composite laminates are emerging as smart structures in automotive and aerospace applications. However, the behavior of the wave propagation within such laminates has not been investigated, which hinders their implementation in Structural Health Monitoring (SHM) and Non-Destructive Evaluation (NDE). Objective: As a result, this manuscript examines the propagation behavior of guided waves in bistable composite structures. By understanding the effect of pre-stressing in bistable composite laminates on the characteristics of propagating waves, such as velocity and amplitude, a more knowledgeable decision about their applications in flaw detection and assessment can be made. Methods: The fundamental symmetric (S0) and anti-symmetric (A0) Lamb wave modes were investigated during propagation in two bistable composite laminates, [0/90]T and [02/902]T, and were assessed experimentally and numerically using ABAQUS. For the tested frequencies, which ranged from 60 kHz to 250 kHz, the behavior of the propagating wave was evaluated for both stable configurations and across two different actuators that were lined up with the fiber directions. Signal processing techniques were thus extensively used to enhance the measured signals and identify both the group velocities and the amplitudes’ trend of the S0 and A0 wave modes. Results: Our results showed that there is a minimal variation (typically below 1%) in the amplitude and velocity of the A0 and S0 modes when the composite plates switch between the first stable configuration and the second stable configuration in both composite plates. These results were numerically validated by replicating the bi-stability of the composites. The numerical data were in relatively close agreement (10% average error) with the experimental values and trends. Furthermore, the bistable effect was examined in detail relative to a reference numerical flat (monostable) plate. Although the bistable effect induced a notable amount of internal residual stress, this did not significantly impact the propagating wave modes, with a maximum difference of about 2% when comparing wave velocities. Conclusions: The effect on the wave propagation behavior along different directions of both stable configurations was shown to be minimal. These results, which were validated numerically, clear the ambiguity on the usage of these laminates in experimental health monitoring.

Correlating in-situ sensor data to defect locations and part quality for additively manufactured parts using machine learning

In this work, process monitoring data, including layerwise imagery, multi-spectral emissions, and laser scan vector data, were collected during laser-based powder bed fusion additive manufacturing and correlated to fatigue performance. All parts were X-ray CT scanned post-build, and internal flaws were identified via an automated defect recognition software. Convolutional neural networks were trained to discriminate flaws from nominal build conditions using in situ data modalities only. Trained classifiers were then tested against a previously unseen data set collected from an independent build, and classification performance and metrics for information content provided by each individual modality were formally established. Correlations were drawn between the detected flaw populations and the corresponding fatigue properties, demonstrating that fatigue critical lack-of-fusion flaws can be detected via machine learning of in situ sensor data. The present results also show that, at least from a classification accuracy perspective, flaw detection via ML on process monitoring data is a viable path forward for real-time flaw detection and automated, interlayer repair strategies. However, strategies for extracting and analyzing sensor data in real-time without incurring excessive increases in build time must first be developed. These developments represent necessary components to draw direct correlations between in situ data modalities, internal part quality, and fatigue performance.

Model of the acoustic path of a separatecombined optical-acoustic transducer

Ultrasonic testing methods occupy one of the key positions in flaw detection, structurescopy, in assessing the strength characteristics of materials and the stress-strain state of products. The method is based on the phenomenon of acoustoelasticity and makes it possible to control the stress-strain state of products by changing the propagation velocity of a longitudinal subsurface ultrasonic wave. To excite acoustic waves, a separate-combined optical-acoustic transducer and a laser-ultrasonic flaw detector are used. The design of a separate-combined optical-acoustic transducer should ensure the measurements accuracy of the time it takes for a longitudinal subsurface wave to reach the receiver of acoustic oscillations. To analyze the recorded acoustic signals and extract from them the signal of a longitudinal subsurface wave, in this work, a finite element model of the acoustic path of a dual-coupled optical-acoustic transducer is proposed and developed. The finite element model was implemented in the COMSOL Multiphysics software package using an explicit solver based on the discontinuous Galerkin method. The developed finite element model makes it possible to visualize the displacement fields of acoustic oscillations, obtain A-scans, and calculate the time of arrival of a longitudinal subsurface wave at the receiver of the optical-acoustic transducer. The calculated values of the arrival time of a longitudinal subsurface wave at the receiver of an optical-acoustic transducer are compared with the results of a full-scale experiment. Calculations and full-scale experiments were performed for steel plates of various thicknesses. The adequacy of the model was confirmed using the Fisher criterion (F-measure). The A-scans obtained as a result of the simulation made it possible to identify the signals recorded by the optical-acoustic transducer: the signal of the longitudinal subsurface wave, the signals of the head and reflected transverse waves, and the intrinsic noise of the optoacoustic transducer. The developed model makes it possible to single out the signal of the longitudinal subsurface wave among the recorded signals of the optical-acoustic transducer. The proposed model can be used in the design of new optical-acoustic transducers, as well as in non-destructive testing (NDT) and materials science.

A new mobile convolutional neural network-based approach for pixel-wise road surface crack detection

In recent years, the worldwide road network has grown rapidly and reached an enormous size. Parallel to this size of the road network, the imperfections on the road surfaces are just as large. This has led to a significant increase in road maintenance costs for traffic management departments. Researchers have proposed methods for automatic flaw detection using deep neural networks to reduce road maintenance costs. However, these methods are insufficient in terms of performance and lightness. In this study, a convolutional neural network-based approach is proposed, which provides more successful road surface crack detection and requires less lightweight compared to other studies. Since the performance of the proposed approach is higher and more lightweight, it can be used more effectively and efficiently in mobile systems.

On the stability of the difference analogue of the boundary value problem for a mixed type equation

This paper considers a difference problem for a mixed-type equation, to which a problem of integral geometry for a family of curves satisfying certain regularity conditions is reduced. These problems are related to numerous applications, including interpretation problem of seismic data, problem of interpretation of Xray images, problems of computed tomography and technical diagnostics. The study of difference analogues of integral geometry problems has specific difficulties associated with the fact that for finite-difference analogues of partial derivatives, basic relations are performed with a certain shift in the discrete variable. In this regard, many relations obtained in a continuous formulation, when transitioned to a discrete analogue, have a more complex and cumbersome form, which requires additional studies of the resulting terms with a shift. Another important feature of the integral geometry problem is the absence of a theorem for existence of a solution in general case. Consequently, the paper uses the concept of correctness according to A.N.Tikhonov, particularly, it is assumed that there is a solution to the problem of integral geometry and its differential-difference analogue. The stability estimate of the difference analogue of the boundary value problem for a mixed-type equation obtained in this work is vital for understanding the effectiveness of numerical methods for solving problems of geotomography, medical tomography, flaw detection, etc. It also has a great practical significance in solving multidimensional inverse problems of acoustics, seismic exploration.

Оцінка можливості застосування зміни розподілу параметрів акустичної емісії для удосконалення визначення стану матеріалів

Technical Diagnostics and Non-Destructive Testing, 2022, №01. TEKHNICHESKAYA DIAGNOSTIKA I NERAZRUSHAYUSHCHIY KONTROL Quarterly scientific-technical and production journal. ISSN: 0235-3474. Established in 1989. Subscription index: 74475. State Registration Certificate KV 4787 of 09.01.2001. All rights reserved Publisher: B.E.Paton Electric Welding Institute, National Academy of Sciences of Ukraine. Main topics of the magazine: theoretical and practical articles on technical diagnosing and non-destructive testing; flaw detection, residual life of structures. Each issue features: scientific articles; manufacturing experiences; reviews; exhibitions, conferences; advertisements.

Оптимізація конструкції вихрострумового перетворювача параметричного типу для виявлення поверхневих тріщин

Technical Diagnostics and Non-Destructive Testing, 2022, №01. TEKHNICHESKAYA DIAGNOSTIKA I NERAZRUSHAYUSHCHIY KONTROL Quarterly scientific-technical and production journal. ISSN: 0235-3474. Established in 1989. Subscription index: 74475. State Registration Certificate KV 4787 of 09.01.2001. All rights reserved Publisher: B.E.Paton Electric Welding Institute, National Academy of Sciences of Ukraine. Main topics of the magazine: theoretical and practical articles on technical diagnosing and non-destructive testing; flaw detection, residual life of structures. Each issue features: scientific articles; manufacturing experiences; reviews; exhibitions, conferences; advertisements.

Automated non-destructive testing of steel alloys microstructure based on multifrequency eddy current method

Technical Diagnostics and Non-Destructive Testing, 2022, №01. TEKHNICHESKAYA DIAGNOSTIKA I NERAZRUSHAYUSHCHIY KONTROL Quarterly scientific-technical and production journal. ISSN: 0235-3474. Established in 1989. Subscription index: 74475. State Registration Certificate KV 4787 of 09.01.2001. All rights reserved Publisher: B.E.Paton Electric Welding Institute, National Academy of Sciences of Ukraine. Main topics of the magazine: theoretical and practical articles on technical diagnosing and non-destructive testing; flaw detection, residual life of structures. Each issue features: scientific articles; manufacturing experiences; reviews; exhibitions, conferences; advertisements.

Застосування фазових характеристик сигналу в автоматизованій вихрострумовій дефектоскопії

Technical Diagnostics and Non-Destructive Testing, 2022, №01. TEKHNICHESKAYA DIAGNOSTIKA I NERAZRUSHAYUSHCHIY KONTROL Quarterly scientific-technical and production journal. ISSN: 0235-3474. Established in 1989. Subscription index: 74475. State Registration Certificate KV 4787 of 09.01.2001. All rights reserved Publisher: B.E.Paton Electric Welding Institute, National Academy of Sciences of Ukraine. Main topics of the magazine: theoretical and practical articles on technical diagnosing and non-destructive testing; flaw detection, residual life of structures. Each issue features: scientific articles; manufacturing experiences; reviews; exhibitions, conferences; advertisements.

Застосування фрактального аналізу при діагностиці технічного стану елементів металоконструкцій

Technical Diagnostics and Non-Destructive Testing, 2022, №01. TEKHNICHESKAYA DIAGNOSTIKA I NERAZRUSHAYUSHCHIY KONTROL Quarterly scientific-technical and production journal. ISSN: 0235-3474. Established in 1989. Subscription index: 74475. State Registration Certificate KV 4787 of 09.01.2001. All rights reserved Publisher: B.E.Paton Electric Welding Institute, National Academy of Sciences of Ukraine. Main topics of the magazine: theoretical and practical articles on technical diagnosing and non-destructive testing; flaw detection, residual life of structures. Each issue features: scientific articles; manufacturing experiences; reviews; exhibitions, conferences; advertisements.

Features of the Propagation of Long Waves in Phononic Crystals, the Influence of the Concentration and Polydispersity of the Components

The relevance of the research is determined by the need to simulate wave propagation in heterogeneous media based on phononic crystals. These studies are necessary for the tasks of flaw detection, the development of non-destructive testing methods, geophysics, engineering geophysics and other methods of acoustic research. During the study, it was determined that the fragmentation of the components of two-component phononic crystals into 2, 3, … N equal parts in the unit cell leads to a catastrophic rearrangement of the dispersion relations and the appearance of new elementary wave packets for acoustic waves. Using numerical-analytical methods, it is shown that in two-component phononic crystals, the polydispersity of the components can significantly affect the average characteristics of the propagation of long acoustic waves when the polydisperse crystal is close to monodisperse with a smaller unit cell size. For polydisperse crystals close to monodisperse with the same period (unit cell size), their averaged characteristics for long waves coincide.

High-Precision Electrical Impedance Tomography for Electrical Conductivity of Metallic Materials

Metal materials are subject to deformation, internal stress distribution, and cracking during processing, all of which affect the distribution of electrical conductivity of the metal. Suppose we can detect the conductivity distribution of metal materials in real time. In that case, we can complete the inverse imaging of metal material properties, structures, cracks, etc. and realize nondestructive flaw detection. However, metal materials' small resistance, high electrical conductivity, and susceptibility of voltage signals to noise signal interference make an accurate measurement of metal conductivity challenging. Therefore, this paper addresses the problem of detecting the conductivity distribution of metals by investigating a high-precision four-electrode AC measurement method. This technical approach combines laminar imaging techniques with high-precision weak signal extraction methods. On this basis, a method and equipment for high-precision electrical impedance tomography of metallic materials’ electrical conductivity were established. The way specifies a new number of electrodes and adopts a model of spaced excitation reference measurements. Single-frequency sinusoidal AC signal is used for excitation, and Shannon wavelet analysis is used for signal extraction and noise reduction. Super-resolution reconstruction algorithms are used for resistivity distribution image reconstruction to improve image quality. Based on the results of various comparative experiments, it is clear that this new functional technique method has good imaging stability and operability and can perform tasks such as analyzing the internal conductivity distribution of metals. This research provides an effective way of new ideas for the safe detection of metal structures, the changes in crystal tissue structure, and the study of metal properties. In particular, it expands the scope of research in the development and application of resistance tomography, which has tremendous commercial potential research significance.

Assessing the influence of CT acquisition parameters on flaw detectability through simulation

X-Ray Computed Tomography (XCT) is a unique tool to fully visualize and understand the nature and size of flaws in industrial parts, with a growing application in different fields such as aeronautics and more recently metal additive manufacturing. The inevitable questions underlying any XCT inspection concern the detectability limit of the measure. What size of defect will be detected with my current configuration? How can I optimize my acquisition material or parameters to improve the detectability limit? Naturally, the known characteristics of the XCT systems (detector pixel size, X-ray tube voltage and focal spot size, magnification) give a first answer to these questions, at least in terms of spatial resolution, but it is more difficult to estimate a priori the visibility of a flaw in terms of contrast. The cost of XCT inspection, the difficulty to design specimen with narrow internal defects and the influence of the geometry of the part on the XCT image quality make experimental analysis of the detectability limits difficult. The simulation brings therefore a promising alternative, provided that it gives a thorough representation of a real inspection.

Nondestructive evaluation of nanostructured components using Eddy current microsensor

Nanostructured materials (NMs) are used in the manufacture of microsensors and some MEMS devices, include nanoscale wires, carbon nanotubes, biomaterials, etc. Nondestructive evaluation of structural integrity of NMs can be carried out by optical (SEM, EBDS) and electromagnetic methods (eddy current). Due to small dimensions of the objects to be tested, the eddy current sensors used in detection of possible discontinuities requires miniaturization so that the answer in amplitude and phase shall allow their localization. In the case of thin tubes made of 304 hard stainless steel, with outer diameters 332μm and wall thickness 38 μm used in medical devices, intended for special catheters, their small dimensions and possible discontinuities require encircling sensor working at high frequencies of the order of MHz. The paper presents the investigation of small diameter tubes using an Eddy current microsensor and the results obtained at the detection of artificial flaws in order to increase the probability of detection (POD), for a high reliability coefficient.

Quantitative study of shaking based on non-destructive testing of steel wire ropes

Abstract Due to the superiority of wire rope in strength, cost, ductility and other characteristics, wire rope has become an irreplaceable material in metallurgy, mining, oil and gas drilling, machinery, chemical industry, aerospace and other fields. Therefore, the wire rope flaw detection is very important work, and in the wire rope detection, jitter is very serious interference. In this article, we firstly discuss the quantitative relationship between displacement and direction of shaking and leakage magnetic field, and this study can also be used as a essential basis for subsequent quantitative studies of local flaws to remove the effect of shaking on leakage magnetic signal.

Simulation and experimental research on 316L liner of bimetal composite pipe with eddy current testing

Conventional magnetic flux leakage technology is unable to identify the stainless steel-lined defects of bimetallic composite pipe. In this paper, the in-line inspection technology of a 316L liner based on the eddy current flaw detection principle is studied with the aim of solving this problem. A printed circuit board (PCB) coil is designed and parameters such as the external diameter, the number of turns, the number of layers and the shape of the eddy current testing coil are determined. The impedance changes that occur under the influence of different factors of the coil are analysed according to finite element simulation. The contribution of each influencing factor during eddy current testing is investigated to optimise the coil parameters and excitation parameters. An eddy current flaw detection model for the stainless steel liner is finally established and numerical calculation is carried out on the impedance response of the defect, which is detected by parameterised scanning with the coil swept. The coil impedance is obtained under the ideal state and then verified by the experimental tests. The results show that the eddy current array testing coil has good applicability in the detection of surface defects on the stainless steel liner, which can provide references for the development of a bimetallic tube composite probe.

Instantaneous lift-off distance estimation in magnetic flux leakage testing of steel wire ropes

Abstract Steel wire rope (SWR) defects plagued its application in many important fields, such as cranes, ports, etc. However, the accuracy of SWRs’ local flaws detection based on magnetic flux leakage (MFL) method is susceptible to the lift-off distance. This paper proposes an instantaneous lift-off distance estimation method to study the effect of lift-off distance on MFL detection for SWR. We firstly derive the relation between the lift-off distance and the MFL field strength based on the magnetic dipole model. After obtaining the time-frequency representation of the MFL signal by using multi-synchrosqueezing transform, we extract the instantaneous amplitude (IA) of the strand signal without other noise interference. Finally, the nonlinear least-squares method is used to fit the relation between the IAs of the strand signals and the lift-off distances to achieve the estimation of the instantaneous lift-off distance. A SWR with three local flaws is tested to validate the proposed method. This work provides instantaneous monitoring of lift-off distances, and it can be used in future noise suppressing methods development or quantitative analysis of local flaws.

Image Defect Recognition Method Based on Deep Learning Network

Deep learning techniques are used to identify weld image defects in the process of image defect recognition. In this paper, a transfer learning method based on convolutional neural networks is proposed for the recognition problem of deep neural network models on weld flaw detection image data sets. Designing interdomain heterogeneous transfer learning with the pretrained model on the large data set, the interdomain heterogeneous transfer learning is used to transfer the pretrained model in the source data domain to the weld inspection image data set according to the difference of the content in the source and target data domains, and the effectiveness of the transfer learning in weld inspection image defect recognition is verified by fine-tuning the whole network by training the parameters of different layers using the frozen layer method. The effect of freezing different layers on the recognition performance of the model is also investigated.

Fault classification based on computer vision for steel wire ropes

Abstract As an actual steel material application, steel wire rope (SWR) is widely used in various industrial cases. However, in real cases, SWRs are inevitably damaged by erosion, friction, etc. Therefore, it is of great importance to monitor SWRs’ surface conditions in case of accidents. In recent years, computer vision recognition of SWR’s surface has been extensively studied. Based on it, we propose an image-processing method to detect local flaws of SWRs, which extracts the features from the illumination area on the surface of the SWR. Firstly, a three-feature dimension descriptor is constructed by combining the edge grayscale value gradient of the illumination area and morphological features of the illumination area. Then, the KNN (k-nearest neighbour) algorithm is used as a classification method to distinguish the local flaws. According to the experiment results, our proposed method has a high accuracy which is about 94.35%, and low time cost. It can be used in future local flaw detection.