Flaw Detection Research Articles

Evaluation of Flaw Detection Algorithm Using Simulated X-Ray Computed Tomography of Ground Truth Data

Abstract A framework to generate simulated X-ray computed tomography (XCT) data of ground truth (denoted here as “GT”) flaws was developed for the evaluation of flaw detection algorithms using image comparison metrics. The flaws mimic some of those found in additively manufactured parts. The simulated flaw structure gave a GT data set with which to quantitatively evaluate, by calculating exact errors, the results of flaw detection algorithms applied to simulated XCT images. The simulated data avoided time-consuming manual voxel labeling steps needed for many physical data sets to generate GT images. The voxelated pore meshes that exactly match GT images were used in this study as opposed to using continuum pore meshes. The voxelated pore mesh approach avoids approximation error that occurs when converting continuum pore meshes to voxelated GT images. Spherical pores of varying sizes were randomly distributed near the surface and interior of a cylindrical part. XCT simulation was carried out on the structure at three different signal-to-noise levels by changing the number of frames integrated for each projection. Two different local thresholding algorithms (a commercial code and the Bernsen method) and a global thresholding algorithm (Otsu) were used to segment images using varying sets of algorithm parameters. The segmentation results were evaluated with various image evaluation metrics, which showed different behaviors for the three algorithms regarding “closeness” to the GT data. An approach to optimize the thresholding parameters was demonstrated for the commercial flaw detection algorithm based on semantic evaluation metrics. A framework to evaluate pore sizing error and binary probability of detection was further demonstrated to compare the optimization results.

Research on Surface Flaw Detection of Industrial Components Based on Deep Learning Algorithm

This paper primarily focuses on the research of industrial component surface defect detection algorithms using deep learning techniques. Taking the example of defect detection in the injector valve seat of automotive engine components, currently, the inspection of such small components heavily relies on manual visual inspection or traditional machine vision methods. Manual visual inspection is inefficient and cannot guarantee the speed and accuracy of detection. Traditional machine vision methods, while efficient, are sensitive to harsh environmental conditions and can be influenced by factors such as lighting, camera positioning, and background, resulting in poor robustness and limited feature extraction capabilities. To address these issues, this paper conducts research on a deep learning-based surface defect detection algorithm for injector valve seats.

Optimal Design of Electromagnetic Tomography Flaw Detection System for Train Wheel Based on FPGA

Electromagnetic tomography (EMT) is a new type of nondestructive testing technology that can be used for metal flaw detection. The implementation of the signal demodulation and filtering algorithm of the current electromagnetic tomography flaw detection system relies on the microprocessor MCU, and the reliability is low in real time. To improve the reliability and system integration of signal processing and transmission of the electromagnetic tomography flaw detection system, this paper uses the field programmable gate array chip to integrate the excitation signal generation module and the digital demodulation module based on the EMT train wheel flaw detection system. A digital filter is designed, and the filter parameters are adjusted to achieve a signal-to-noise ratio of 15 dB. UDP communication protocol is utilized to realize Ethernet communication between FPGA and upper computer. Using three algorithms on the host computer for image reconstruction, the train wheel defect information is visualized. The experimental results show that the FPGA chip improves the immunity of the train wheel flaw detection system, realizes the high integration of the system, and achieves the purpose of train wheel defect detection.

Flame retardant, strong and tough, low dielectric epoxy thermoset enabled by a novel bi-DOPO type epoxy with aggregation-induced emission

Multifunctional epoxy resins with excellent flame retardancy, high strength, high toughness, low dielectric loss and strong fluorescence show great potentials in electrical/electronic applications; however, simultaneously achieving those goals are difficult. To address those challenges, an epoxy resin with 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) pendant group and flexible aliphatic spacer (DOPOEP) was developed. V-0 rating in UL-94 test and LOI of 33.1% are achieved at only 0.45 wt% phosphorus loading, benefitting from the flame retardant mechanism in gas and condensed phase. Compared with neat DGEBA, the cured epoxy with 10 wt% DOPOEP shows 13.4% and 37.6% improvements in tensile strength and impact strength, respectively; in addition, the tensile strength of the cured epoxy with 20 wt% DOPOEP is as high as 102.2 MPa, a 35.3% increase, without compromising toughness. The strengthening effects of DOPOEP derive from π-π stacking and hydrogen bonding, and the improved toughness is due to the flexible aliphatic spacer and reduced crosslinking density. Meanwhile, free volume reduction was observed after introducing DOPOEP, which is responsible for the improved modulus. DOPOEP addition also lowers the hydroxyl group concentration, leading to lower dielectric constant and dielectric loss tangent. Moreover, the aggregation-induced emission behavior of DOPOEP was reported, and its application in anti-counterfeiting field was explored. In air, the fluorescence of DOPOEP-containing thermoset steadily diminishes with increasing temperature, however the fluorescence changing trend is complex in vacuum. Besides, the fluorescence of DOPOEP is effective in the visual detection of defects and flaws in cured products. This work provides a new direction for developing high-performance epoxy with multifunction.

In-situ monitoring of image texturing via random forests and clustering with applications to additive manufacturing

The amount of attention paid to in-situ monitoring in Additive Manufacturing (AM) has significantly increased over the last few years, paving the way to a paradigm shift for quality monitoring and control via big data analysis of signals, images, and videos. In-situ quality monitoring represents an opportunity for waste reduction and first-time-right production via inline detection of process flaws, which allows early identification of scraps and the possibility of correcting actions for first-time-right production. This article presents a solution for in-situ monitoring of images taken layerwise in material extrusion AM. Compared with the existing solutions, mainly focusing on monitoring the shape deviation observed at each layer with respect to the nominal shape, this article focuses on monitoring the internal surface texture with the aim of detecting over- and under-extrusion flaws. Inspired by an approach reported in the literature that was developed for textile image monitoring, we propose a solution for in-situ monitoring of textured surfaces which is based on combining Random Forests with clustering to automatically identify defective locations layerwise. A real case study based on Fused Filament Fabrication is used to compare the performance of the novel proposed solution with the original one and identify an appropriate direction for future research.

Bond-slip response of end-hook and half-hooked steel fibers in moderately strong SIFCON matrix

In this study, an attempt was made to study the bond-slip performance of end-hook and half-hooked steel fibers embedded in moderately strength SIFCON matrix, to find out the cause of excessive mechanical anchorage and to further optimize the bond-slip equation of steel fibers. Used end-hook steel fibers with embedded depths 10 and 15 mm, pullout offset angles 0°, 30°, 45°, and 60°; Half-hooked steel fiber with embedded depths 10 and 15 mm, end-hook bending angles 30°, 45°, 60°, and 90°; Novel end-hook and novel half-hooked steel fibers with end-hook length doubled, which were subjected to a static pullout test at a rate of 0.01 mm/s. Experiments showed that the half-hooked steel fiber took the best end-hook bending angle at 60°; The novel end-hook and half-hooked steel fiber had a good bonded performance. The single steel fiber anchorage performance evaluation index K was proposed. Through experimental phenomena, data analysis, and X-ray flaw detection, the existence of “local damage” was proved. Based on the traditional bond-slip formulation, a new bond-slip formulation for moderately strength SIFCON substrates is proposed by adding a plastic hinge in the “local damage” region. By comparing the old and new equation, it was found that the calculation error of the maximum pullout load of end-hook steel fibers was reduced by 3.77 times and that the calculation error of the maximum pullout load of half-hooked steel fibers was reduced by 2.8 times, and the calculation equation for the peak load of secondary reinforcement of half-hooked steel fiber was supplemented, which is of application value for further optimization of the shape of steel fibers.

Visualized X-ray Dosimetry for Multienvironment Applications.

X-ray dose detection plays a critical role in various scientific fields, including chemistry, materials, and medicine. However, the current materials used for this purpose face challenges in both immediate and delayed radiation detections. Here, we present a visual X-ray dosimetry method for multienvironment applications, utilizing NaLuF4 nanocrystals (NCs) that undergo a color change from green to red upon X-ray irradiation. By adjustment of the concentrations of Ho3+, the emission color of the NCs can be tuned thanks to the cross-relaxation effects. Furthermore, X-ray irradiation induces generation of trapping centers in NaLuF4:Ho3+ NCs, endowing the generation of mechanoluminescence (ML) behavior upon mechanical stimulation after X-ray irradiation ceases. The ML intensity shows a linear correlation with the X-ray dose, facilitating the detection of delayed radiation. This breakthrough facilitates X-ray dose inspection in flaw detection, nuclear medicine, customs, and civil protection, thereby enhancing opportunities for radiation monitoring and control.

REVIEW ON NON-DESTRUCTIVE TECHNIQUES FOR EARLY FLAW DETECTION IN INSPECTIONS

Non-destructive testing (NDT) is critical in ensuring various materials’ and structures’ integrity and safety by detecting flaws without causing damage. Early flaw detection is essential for preventing catastrophic failures, reducing maintenance costs, and enhancing operational efficiency. This research paper comprehensively reviews non-destructive techniques for early flaw detection in inspections. The paper begins with an introduction to the significance of flaw detection and the motivation behind employing non-destructive techniques. It proceeds with a thorough literature review, summarizing existing research and highlighting the strengths and limitations of different methods. The main section of the paper delves into the description and principles of common non-destructive techniques, including Ultrasonic Testing (UT), Radiographic Testing (RT), Magnetic Particle Testing (MPT), Liquid Penetrant Testing (LPT), Eddy Current Testing (ECT), Thermography, Acoustic Emission Testing (AET), and others. The efficacy of each technique is explored through various case studies and real-world applications across diverse industries. Advantages, limitations, and factors influencing technique selection in specific scenarios are critically analyzed. Furthermore, the paper discusses recent advancements and emerging technologies in non-destructive testing that offer potential improvements for early flaw detection. It also covers relevant international and industry-specific standards and regulations governing NDT, emphasizing their significance in enhancing inspection reliability. The conclusion reaffirms the importance of non-destructive techniques for early flaw detection. It proposes future research directions to overcome existing challenges. This review aims to provide engineers, researchers, and practitioners with valuable insights into the state-of-the-art NDT methods, fostering continuous improvement in early flaw detection and ensuring the safety and reliability of critical infrastructures and materials.

Efficient multivariate optimization of an ultrasonic transducer with genetic parallel algorithms

Design optimization of a multilayer transducer is desired for flaw detection, biomedical and underwater applications by controlling layer thicknesses of active material and volume fractions of ceramic in each piezocomposite layer. To handle the computational complexity in solving such multivariate optimization and the complex nature of the problem, we propose a parallel genetic algorithm (GA)-based strategy. The complexity of the problem increases with the number of layers and layer-wise ceramic volume fraction to achieve an optimized transducer working efficiently in an operational frequency range as desired by the application specification. The fitness function is formulated based on the transducer response in terms of about the same value of pressure magnitude at the first three harmonics at functional frequencies. The fitness value is computed using a one-dimensional model (ODM) for PMNPT, PZT4d, and PZT5h-based materials initially, while input layer thicknesses and volume fractions are evaluated through GAs and passed to ODM. The overall scheme is maintained through parallel GAs. The simulation is performed by using 1, 4, 6, and 8 core machines to reduce time complexity. The results for parallel GA-ODMs are statistically represented in the minimum, maximum, mean and standard deviation of fitness, while graphically represented for speedup and time.

Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning

Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. However, the current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques, which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. In this work, an in situ NDE (INDE) system was engineered to detect subsurface flaws detected in X-Ray Computed Tomography (XCT) directly from process monitoring data. A multilayer, multimodal data input allowed the INDE system to detect numerous subsurface flaws in the size range of 200–1000 µm using a novel human-in-the-loop annotation procedure. Furthermore, a framework was established for generating probability-of-detection (POD) and probability-of-false-alarm (PFA) curves compliant with NDE standards by systematically comparing instances of detected subsurface flaws to post-build XCT data. We also introduce for the first time in the AM in situ sensing literature the a90/95 – the flaw size corresponding to a 90% detection rate on the lower 95% confidence interval of the POD curve. The INDE system successfully demonstrated POD capabilities commensurate with traditional NDE methods. Traditional ML performance metrics were also shown to be inadequate for assessing the ability of the INDE system’s flaw detection performance. It is the belief of the authors that future studies should adopt the POD and PFA approach outlined here to provide better insight into the utility of process monitoring for AM.

THREE-FREQUENCY METHOD FOR DETERMINING THE EQUIVALENT SIZE OF THE DISCONTINUITY DURING MANUAL ULTRASOUND CONTROL

The work is devoted to topical issues of increasing the reliability and informativeness of the results of manual ultrasonic inspection of parts and assemblies of technical objects. The paper presents a three-frequency method of ultrasonic flaw detection, which makes it possible to increase the reliability of the non-etalon method for determining the type of discontinuity detected as a result of ultrasonic testing. The developed method makes it possible to determine the type of discontinuities of various types. The NDTRT-26 software product has been developed to automate calculations using the three-frequency method.

A Machine Learning Approach for Automated Detection of Critical PCB Flaws in Optical Sensing Systems

The circuit boards in the fields of optical sensors and optical devices require extremely high levels of precision and performance. For instance, applications such as fiber optic communication, optical computing, biomedical devices, and high-performance computing devices all necessitate meticulous optical components. Any minute defect on the circuit boards of these components has the potential to adversely impact the performance of the entire device. Traditional circuit defect detection methods require manual inspection, which is very labor-intensive and time-consuming. The defect detection method based on deep learning can automatically learn features and more accurately find defects in printed circuit boards, improve detection efficiency, and reduce the workload, bringing better economic and social benefits. Based on the popular YOLOv8 model, this paper uses the open-source circuit defect dataset, introduces Wise IoU, proposes the W–YOLOv8 model, and uses the gradient gain allocation strategy of a dynamic non-monotonic focusing mechanism to make the model focus on ordinary-quality anchor boxes, which improves the performance of the original model. Experimental data show that the mAP50 of W–YOLOv8 is 97.3%, which is 1.35% higher than that of YOLOv8, and the mAP50-95 is 55.4%, which is 3.94% higher than that of YOLOv8.

Scientific Research on a Gold- and Silver-Inlaid Bronze Zun from the Han Dynasty

The bronze Zun was one of the more prevalent high-class wine containers of the Han dynasty, representing the highest level of decoration in bronze at the time. However, little has been reported about its technical characteristics and scientific value. In this paper, the samples were selected for analysis based on scientific analysis, following the principle of “minimal intervention”, and a bronze Zun from the Han dynasty in the Gansu Provincial Museum collection was studied using ultra-deep field microscopy, X-ray flaw detection, X-ray fluorescence spectrometry (XRF), scanning electron microscopy (SEM), and energy spectrometry (EDS). The results show that the gold and silver decoration on the bronze is inlaid rather than gilt. Secondly, the body and lid of the vessel are molded in one shot, with the bird-head-shaped and animal-foot-shaped components cast separately and then attached to the lid and body. Thirdly, the corrosion of the bronze Zun is characterized by the copper matrix being corroded first and most severely, followed by the silver and, finally, the gold. The high purity of the gold wire embedded in this bronze Zun, the fine width of gold wire (154–190 μm), and the magnificent decoration show the excellent processing technology level of the precious metal and the high aesthetic level of ancient man during the Han dynasty. The results of the analysis of this bronze Zun can provide an essential reference for research on bronze vessels of the same type, the techniques of gold and silver misalignment, and the development of the history of bronze manufacture and technology during the Han dynasty.

Virtual round robin 2 – Phased array inspection of dissimilar metal welds

Round-robin exercises have traditionally been laborious to arrange in non-destructive testing (NDT). The exercises have involved manufacturing of costly big mock-ups and then distributing them around the world to facilitate testing by numerous laboratories. This has limited both the number of such round robins and their scope. Often the round robins have contained small number of flaws and the representativeness of these flaws has been limited. Nevertheless, the few round robins that have been completed have yielded significant additional understanding on the capability of the used NDT methods and procedures.Recently, the increased use of automated inspections together with the development of virtual flaws (independently by Trueflaw and EPRI) has enabled a new type of round robin, where instead of moving samples around the world, the round robin is focused on the data analysis and only pre-acquired data files are distributed. In 2019–2020, first of a kind virtual round robin (VRR) was completed. The round-robin allowed for the first time to compare inspection performance from teams around the world with statistically significant number of flaws and with ultrasonic data representative for nuclear dissimilar metal weld inspection. The study resulted in important new insight into NDE reliability for nuclear applications.However, as a first of a kind study, the first virtual round robin also contained some significant limitations. In particular, the data sets distributed were limited in order to limit the effort needed from each participating inspector. The reduced amount of data acquired was compensated by using pre-optimized data gathering, possible only with prior knowledge on the flaws present. While these choices were well justified for the first round-robin, they also made direct comparison of VRR results and real-life inspector performance problematic. In addition, the first VRR focused primarily on flaw detection and the data was insufficient for sizing.To address these shortcomings of the first round robin, a second round robin was completed in 2021–2022. In this second round robin, more representative data was used for evaluation. In addition, increased emphasis on the hard-to-detect small flaws was put forward to get improved into detectability especially in the low end.The more representative data required much more significant effort from the inspectors, which reduced the participation as compared to the first round robin. Furthermore, the emphasis on difficult-to-detect cracks may have further deterred participation, as the exercise may have been seen as too challenging. While the number of downloaded data sets (23) was similar to previous exercise, the number of returned sets was reduced to 5, compared to previous 18. Despite the smaller than expected participation, the results revealed several interesting features. The results displayed marked variation. Also, the false call rate was significantly reduced, as compared to the previous study. This could be attributed to the more rich data set, which allowed more comprehensive evaluation and exclusion of potential false calls.The recent advances in machine learning (ML) for ultrasonics also introduced an interesting opportunity to compare machine learning results with the human inspectors. Developing an optimized machine learning model for the present data was outside the scope of this study. Instead, an independently developed model, if somewhat sub-optimal, was used. Thus, the results should not be taken as a measure of ML performance as such. Nevertheless, the comparison between human results and ML model are informative and illustrate the potential benefits of automated data evaluation.

Method for Concrete Structure Analysis by Microscopy of Hardened Cement Paste and Crack Segmentation Using a Convolutional Neural Network

In recent years, the trend of applying intelligent technologies at all stages of construction has become increasingly popular. Particular attention is paid to computer vision methods for detecting various aspects in monitoring the structural state of materials, products and structures. This paper considers the solution of a scientific problem in the area of construction flaw detection using the computer vision method. The convolutional neural network (CNN) U-Net to segment violations of the microstructure of the hardened cement paste that occurred after the application of the load is shown. The developed algorithm makes it possible to segment cracks and calculate their areas, which is necessary for the subsequent evaluation of the state of concrete by a process engineer. The proposed intelligent models, which are based on the U-Net CNN, allow segmentation of areas containing a defect with an accuracy level required for the researcher of 60%. It has been established that model 1 is able to detect both significant damage and small cracks. At the same time, model 2 demonstrates slightly better indicators of segmentation quality. The relationship between the formulation, the proportion of defects in the form of cracks in the microstructure of hardened cement paste samples and their compressive strength has been established. The use of crack segmentation in the microstructure of a hardened cement paste using a convolutional neural network makes it possible to automate the process of crack detection and calculation of their proportion in the studied samples of cement composites and can be used to assess the state of concrete.

Optimization of the Parameters of the Magnetizing Device of the Electromagnetic-Acoustic Transducer

Contactless electromagnetic-acoustic transducers have a set of significant advantages over contact transducers, but at the same time they have significant disadvantages that require the development of effective magnetizing devices. Compared to magnetizing devices that are using permanent magnets, electric current magnetization devices are easily removed from the object of testing and cleaned from contamination by metal particles. Unfortunately, such transducers have significant dimensions and weight.A transducer containing a magnetic circuit magnetized by an electric current coil and two independent electromagnetic inductors located in the gap between the central part of the magnetic circuit and the object of testing has been developed. Inductors are two flat coils, each of them has form like a butterfly. The inductor conductors located in the working area have mutually perpendicular directions; they allow exciting and receiving the transversely polarized acoustic waves without rearranging the transducer. In order to reduce the overall dimensions and mass of the transducer, the mass and dimensional parameters of the magnetizing device were optimized for operating conditions when the magnetization of the object of testing and measurement are performed during the active measurement phase. During the passive measurement phase, which is three times longer than the active phase in time, the magnetizing device cools down. The cyclic mode with alternating active and passive phases made it possible to reduce the weight of the transducer by more than 3 times. In the working area of the transducer with a size of 15×15 mm, with a gap of 1 mm between the magnetic field concentrator and the object of testing, a field with a normal component of 2.4 T is created. The transducer has protection of the magnetization device from overheating, and the cyclic mode of operation allows for continuous performance of up to 30 measurements per minute at an ambient temperature of 20 °C.The developed magnetizing device can be used in solving a number of problems of structuroscopy, thickness measurement, flaw detection by electromagnetic-acoustic methods based on accurate measurement of the propagation time of elastic waves in the object of testing.

Infrared in-line monitoring of flaws in steel welded joints: a preliminary approach with SMAW and GMAW processes

The non-destructive full-field non-contact thermographic technique is applied for non-destructive flaw detection of the welded joints, in real-time and offline configuration. In this paper, a thermographic procedure for real-time flaw detection in manual arc welding process is presented. Surface temperature acquisitions by means of an IR camera were performed during arc welding process of 8 specimen both for calibration and validation of the numerical model. The investigated variables are the technique (manual stick arc (SMAW) and gas arc (GMAW) welding) and the joint shape (butt and T joint) for steel joints, in sound conditions and with artificial flaws. Numerical simulation of welding thermal transients was run to obtain the expected surface temperature fields and thermal behavior for different welding parameter configurations. Hardness measurement and micro-graphic analysis were performed to validate numerical simulation results. The real-time thermographic study of the weld pool gives direct indications of anomalies; local studies of the thermal transient and thermal profiles can detect some kind of flaws; microstructural analysis of Heat-Affected Zone (HAZ) and surrounding areas higlights the presence of austenite and martensite distribution which justifies the thermal transients and thermal profiles for different welding configurations. Comparing real-time IR acquisition of the welding process with simulated thermal contours of sound processes provides information of presence of some kind of flaws. Since most of the flaws are generated in the weld pool, it is possible to recognize anomalies directly from the thermal acquisitions or with post-processing the acquired data.

Noncontact nondestructive ultrasonic techniques for manufacturing defects monitoring in composites: a review

Composite materials are widely used in most industries due to their high specific strength, specific stiffness, and their relatively lighter weight compared to other traditional materials. However, the presence of defects arising from manufacturing processes or during service loads can make these structures more susceptible to a diminished performance. Furthermore, the former defects are inevitable in composite structures, but they can be reduced. Each type of defect requires specific inspection techniques and configurations. In this work, a review of the different types of composites manufacturing processes and their corresponding resultant defects is presented with the various nondestructive evaluation techniques employed for these defects’ characterization. The emphasis of this paper is on ultrasonic inspection and detection techniques for they present high sensitivity to surface/subsurface discontinuities, superior depth of ultrasonic penetration for flaw detection, feasibility on large scales, and instantaneous and detailed images production. Notably, noncontact ultrasonic testing techniques are also reviewed, air-coupled techniques in specific, and highlighted as a fine alternative to conventional contact inspection systems as they reduce the restrictions that coexist with the use of couplants. Moreover, these ultrasonic testing techniques are summarized to show the latest research progress achieved in the field of air-coupled ultrasonic inspection systems for manufacturing defects’ monitoring in composite structures including delamination, porosity, dryness, waviness, and resin lack/excess. Finally, we highlight the type and central frequency of the transducers and experimental results present in literature and obtained in terms of both detection and size of the defects.

Detection of flaws in austenitic stainless steel plate using eddy current testing

This study proposes an eddy current testing (ECT) probe to detect flaws in finite thickness austenitic stainless-steel plates. In order to obtain the spatial magnetic field distribution and the detection sensitivity of the probe in different scanning directions, a 3D model of ECT is simulated. The detection ability of ECT probe is verified by experiment. To detect surface flaws and subsurface flaws, 2.0 kHz and 1.0 kHz are chosen as excitation frequencies respectively. In addition, the pick-up signal goes through the amplification circuit and the signal conditioning module to improve the signal-to-noise ratio. Finally, the experimental results are compared with those from numerical simulation. Both results show the ability of the probe to detect flaws in the austenitic stainless-steel plate.

High-speed, multi-zone ultrasonic inspection of bar and wire stocks with an in-line phased array inspection system

Typical standards in aerospace applications require the ultrasonic testing (UT) of bar and wire stock with a highly reliable detection of small defects over a wide depth range in the material. Characteristic reference reflectors are flat bottom holes (FBH) with diameters smaller than 1 mm. Typically, such inspections are done with immersion tanks using a set of probes optimized for each depth. However, for industrial applications there are disadvantages such as slow scan speed, the time-consuming inlet and outlet process, complicated mechanics and significant costs of the specialized probes. This contribution demonstrates an in-line phased array inspection system with integrated multi-zone inspection, which is based on the Krautkrämer ROWA™ Ux machine. After a short description of the setup special focus is given on the implemented testing concept which is optimized with respect to a multi-zone inspection and includes a dedicated automated adjustment of the system. Furthermore, the powerful electronics allows a flexible setting of focus point, aperture size and filtering. This significantly reduces the need for special probes and by means of digital depth focusing (DDF) also the number of test shots. At the end, the flaw detectability in different depths is illustrated and achievable test speeds are presented. Moreover, external boundary conditions are discussed.