Inspection Of Plates Research Articles

Ultrasonic imaging of concrete-embedded corroded steel liners using linear and nonlinear evaluation

ABSTRACT This study explores the efficacy of horizontally polarised shear (SH) waves in ultrasonic inspection of concrete-embedded steel liner plates, focusing on corrosion detection. SH-waves are found to significantly improve the localisation and potential dimensional assessment of embedded plate-like structures compared to compressional (P) waves, as evidenced by SH C-scans. Anomalies corresponding to severe corrosion are discernible in SH C-scans, whereas they are minimally visible in P-wave scans. Nonlinear evaluation explores harmonic-based imaging, including second harmonic generation (SHG) and third harmonic generation. Nonlinear SH-wave evaluation accurately locates the most severely corroded embedded plate, whereas comparative P-wave evaluations fail. However, potential FFT sidelobe contamination in the results warrants consideration. These results suggest a promising use for nonlinear waveform distortion in SH-wave-based corrosion detection though conclusive evidence of the source of SHG is uncertain. Experiments utilised an automatic scanner system for consistent control. The concrete specimen investigated herein contained pre-corroded plates which hint at heightened nonlinear effects when corrosion occurs post-embedment due to corrosion-induced cracking. These findings encourage further investigation into SH-waves for nonlinear ultrasonic detection of concrete-embedded steel liner plates and the development of instrumentation tailored to such methods.

Crossflow microsand filtration in cooling tower systems to control fouling in heat exchanger devices

In the context of global warming, there is a growing need for high-performance heat, ventilation and air conditioning systems. Fouling is a challenge, affecting heat exchanger operational and energetic performance. Side stream filtration can be used to improve water quality by reducing particle concentration in the system. The aim of this study was to assess a crossflow microsand filtration unit's efficacy in reducing fouling within an 81-plate heat exchanger, in an in-situ cooling tower circuit. A pilot heat exchanger without filtration was operated under similar conditions. After 3 years of continuous operation, visual inspection of the full-scale heat exchanger plates operating with filtration showed minimal accumulation. Pairs of facing plates (7) were scraped and rinsed to collect total solids accumulated on surfaces. Samples were resuspended and analyzed for solids and metal concentration measurements. Low average values of total volatile solids (45 μg/cm2) and total solids (93 μg/cm2) were indicative of a low biofilm formation. X-ray diffraction revealed the presence of calcite associated to water hardness. Low levels of iron and copper were detected despite slightly corrosive water. In contrast, the pilot heat exchanger exhibited higher deposit accumulation of total volatile solids (90 μg/cm2) and total solids (110 μg/cm2) after only 12 weeks of operation. These findings underscore the role of side stream filtration in reducing particle deposition and biofouling potential in full-scale cooling tower systems. Crossflow microsand filtration is a promising avenue to control fouling in building cooling tower systems, which could contribute in improving the building's energetic performance.

Guided Wave Phased Array Ultrasonic Imaging for Thin Plate Inspection

This study focuses on the application of phased array ultrasonic testing with guided waves for Structural Health Monitoring (SHM) in thin plates. Addressing the limitations of conventional ultrasonic testing efficiently by inspecting thin, long structures. This study employs phased array technology to thin isotropic plates at high frequency(2MHz), thereby elevating imaging and defect detection in structural components. Through the utilization of the full matrix capture (FMC) scanning approach in 3-D finite element (FE) simulations, A0 and S0 modes were generated on steel plates with through-hole geometries. The process involved capturing reflected signals to form an FMC matrix that includes all A-scan signals. Subsequently, the total focusing method (TFM) algorithm was applied to FMC data for the reconstruction of focused images of A0 and S0 modes separately. A key improvement in our approach is integrating a time-domain filtering method into the FMC data. This enhancement addresses the dispersive nature of guided waves, thus improving the accuracy in identifying defect locations and sizes within the images. Our finite element simulations on steel specimens, with varied through-hole locations, revealed the method's potential in accurately imaging defects located deeper within the structure. Additionally, the study expanded to evaluate the efficacy of phased array ultrasonic testing (PAUT) in SHM, particularly in conjunction with guided waves to study the flaw detection sensitivity of the defect through the TFM algorithm on isotropic steel plates up to wavelength(λ)/16. This whole analysis considered crucial factors such as ultrasonic wave mode, frequency, and group velocity for the structure under inspection. This study represents a significant contribution to the field of SHM, introducing a sophisticated, real-time monitoring methodology that promises to enhance structural integrity assessment. Keywords: SHM, PAUT, GW, FMC, TFM FE Simulations, Time Domain Filtering, Ultrasonic Testing.

Hyper-Progressive Real-Time Detection Transformer (HPRT-DETR) algorithm for defect detection on metal bipolar plates

Aiming to address common defects such as scratches, cracks, bumps, and indentations on the surface of metal bipolar plates, this thesis proposes an algorithm called HPRT-DETR for detecting defects on metal bipolar plates. The algorithm aims to address issues such as small defects, complex backgrounds, and low detection accuracy. To enhance the performance of the algorithm, we adopt the DA to improve the AIFI module. This enhancement enables the algorithm to focus on the defective region, helping it capture more informative features. Meanwhile, we have implemented Zoom-cat scaling splicing and SSF to enhance the multi-scale feature fusion capability of the network in the CCFM module. Additionally, we have introduced the NWD metric loss to reduce sensitivity to small target locations, thereby improving detection accuracy and efficiency. Experimental validation shows that the enhanced HPRT-DETR model achieves improvements of 6.4, 1.7, and 4.7 percentage points in accuracy, recall, and average precision, respectively, compared to the original model. These results indicate that the enhanced model lays the foundation for automated production and intelligent inspection of metal bipolar plates.

Improving defect detection capability of pulse and pulse phase thermography method for CFRP plates by enhancing rear surface heat transfer coefficient

Infrared pulse thermography (PT) is a noncontact and convenient nondestructive inspection method, and its application to various objects has been reported in many papers. However, conventional PT is not suitable for detecting deep- or near-rear-surface defects. This study focuses on PT inspections of CFRP plates and improvements in their defect detection capabilities. To improve the capability of detecting near-rear-surface defects, a method for enhancing the heat transfer coefficient (HTC) of the rear surface during PT inspection was examined in this study; this would increase the temperature difference in the defective areas observed in thermal images. To investigate the effectiveness of the proposed method, experiments were conducted on CFRP specimens with artificial defects. The CFRP specimens were inspected using the PT method, during which water was brought into contact with the rear surfaces of the specimens to enhance the HTC. The experimental results showed that the capability of detecting near-rear-surface defects improved when the rear surfaces were in contact with water with forced convection. In addition, obtaining phase images by applying a Fourier transform to the experimentally obtained temperature data (that is, pulse phase thermography: PPT) was examined. Consequently, the combination of the enhancement of the rear-surface HTC and the use of phase images was significantly effective in improving the defect detection capability.

TOFD detection of shallow subsurface defects in aluminum alloy thin plates by half-skip mode-converted wave

Aluminum alloy is widely applied to the aerospace field. However, the inspection of thin plates using Time-of-Flight Diffraction (TOFD) technique is restricted by the near-surface dead zone because of the coupling between diffracted longitudinal wave and lateral wave. The half-skip mode-converted wave is introduced to decrease dead zone and detect defects in aluminum alloy thin plates by increasing ray path and propagation time. The quantitative correlation for the diffracted shear wave from longitudinal back-wall wave is deduced in combination with the acoustic path, realizing the accurate location of shallow subsurface defects. Simulated and experimental results indicate that the dead zone is decreased by 38% by the half-skip mode-converted wave, and the location errors are within 5% for the aluminum alloy plate with a thickness of 7.0 mm. Compared to other alternative TOFD techniques, half-skip mode-converted wave has better response amplitude and positioning accuracy, demonstrating strong applicability in TOFD inspection of thin plates.

Research on the mechanism of electromagnetic ultrasonic energy transfer based on dynamic multi-magnetic vector coupling

The traditional electromagnetic acoustic transducer (EMAT) in steel plate inspection has a problem of suboptimal energy transfer efficiency due to fixed static sensitivity. The dynamic sensitivity detection method with multi-magnetic vector coupling is proposed based on the principle of mutual conversion of electromagnetic fields inside and outside the workpiece. The conversion relationship of multi-magnetic vector coupling was analyzed, and then a mathematical model of rotational wave displacement between wave displacement and magnetic parameters was established. This approach theoretically guides the modeling, simulation, and design optimization of high energy conversion efficiency EMATs. The experiment shows that under the action of rotating magnetic field, different rotating directions and deflection angles have varied detection effects. The optimal energy transfer efficiency of the transducer under the dynamic multi-magnetic vector coupling mechanism is effectively improved compared with the static traditional form. The optimal signal amplitude of the dynamic sensitivity detection method is 3.73 times that of traditional detection signals. The electromagnetic ultrasonic dynamic multi-magnetic vector coupling detection method explores new ideas for the mechanism analysis from static to dynamic and guarantees high sensitivity detection and evaluation of steel plates.

Evaluation of Welding Imperfections with X-ray Computed Laminography for NDT Inspection of Carbon Steel Plates

Evaluation of Welding Imperfections with X-ray Computed Laminography for NDT Inspection of Carbon Steel Plates

Latest Developments in the Hardspot Inspection of heavy plates

Hard Spots are local areas with increased hardness on the surface of semi-finished or end products in steel manufacturing. The cause of these hard spots is attributed to effects in the casting or rolling process. In general, the occurrence of hard spots cannot be avoided completely in production of heavy plates. Therefore, in various current line-pipe specifications the topic ‘hard spots’ is discussed and there is actually a strong request for a suitable test method from end users as well as manufacturers of line pipes. The current presentation should give an update on the ongoing developments. Basis of all current approaches are electromagnetic inspection methods, based on the correspondence in between electrical and magnetic material properties and physical hardness. To be able to integrate an inspection into the production process, the so called rolling skin or mill scale, a thin surface layer of different iron oxides with different material properties to the base material, should not influence the inspection results. The inhomogeneities of this rolling skin, especially in conductivity and permeability, normally render standard eddy current inspection impossible. Additionally in the standard production process, the products were handled by large cranes, manipulating the plates by strong magnets. These magnets leave behind a significant residual magnetization. To overcome both problems the method uses a strong alternating magnetization to measure different resulting micro magnetic parameters. By combining these parameters, the mill scale as well as the residual magnetization effects can be minimized, so it gets possible to measure small local hardness variations on the plates. The presented paper describes the actual progress of the inspection method itself including the theoretical background as well as the calibration on artificial hard spots. Furthermore, an outlook will be given to the implementation in an automatic inline inspection system for plate mills.

Crystal search - feasibility study of a real-time deep learning process for crystallization well images.

To avoid the time-consuming and often monotonous task of manual inspection of crystallization plates, a Python-based program to automatically detect crystals in crystallization wells employing deep learning techniques was developed. The program uses manually scored crystallization trials deposited in a database of an in-house crystallization robot as a training set. Since the success rate of such a system is able to catch up with manual inspection by trained persons, it will become an important tool for crystallographers working on biological samples. Four network architectures were compared and the SqueezeNet architecture performed best. In detecting crystals AlexNet accomplished a better result, but with a lower threshold the mean value for crystal detection was improved for SqueezeNet. Two assumptions were made about the imaging rate. With these two extremes it was found that an image processing rate of at least two times, but up to 58 times in the worst case, would be needed to reach the maximum imaging rate according to the deep learning network architecture employed for real-time classification. To avoid high workloads for the control computer of the CrystalMation system, the computing is distributed over several workstations, participating voluntarily, by the grid programming system from the Berkeley Open Infrastructure for Network Computing (BOINC). The outcome of the program is redistributed into the database as automatic real-time scores (ARTscore). These are immediately visible as colored frames around each crystallization well image of the inspection program. In addition, regions of droplets with the highest scoring probability found by the system are also available as images.

PHM-IRNET: Self-training thermal segmentation approach for thermographic inspection of industrial components

Automating thermographic analysis for industrial and construction inspection is one of the essential aspects of NDT 4.0 in industrial transformation. Thermal segmentation is a preliminary step for characterizing a component’s heat signatures by detecting and extracting thermal patterns. Manual segmentation of areas with distinctive thermal patterns is a time-consuming task that consequently can increase the operation cost. Thus, automatic thermal segmentation can significantly assist companies in avoiding possible misinterpretation and decrease the operation cost. Due to the lack of enough labeled thermal images to train deep neural networks and the fact that, by nature, thermal abnormalities may have non-uniform patterns and form in different shapes and sizes, training a deep neural network can be a challenging task. Moreover, selecting common features can cause possible convergence onto dependency toward specific shapes or patterns. Thus, this paper proposes a self-training thermal segmentation method involving a novel process pipeline for using labeled and unlabeled data for training the presented deep-learning model. The method uses an unsupervised approach for predicting the ground truth in case of unlabeled data before passing the data to the secondary model for segmentation. For this paper, three experiments were conducted to evaluate the method’s performance and provide a comparative analysis with the most recent techniques. The first experiment used a simulated inspection created by COMSOL software. The second experiment employed several plate inspections in several isolated environments. Finally, the third experiment used thermal images acquired during an actual inspection of the piping structure and aerospace components.

Robotic Positioning for Quality Assurance of Feature-Sparse Components Using a Depth-Sensing Camera

In this paper, a novel method for crawler positioning is presented utilizing an onboard depth-sensing camera which can operate in semi-structured, self-similar environments, using only measurements of the sample under inspection to navigate. Non-destructive evaluation at manufacture is a vital aspect of assuring the fitness for purpose of high-value marine assets, moreover, it is almost always a regulatory requirement to ensure build quality standards have been met. Traditionally these inspections were deployed manually by a trained operator in a laborious and time-consuming manner. More recently, robotic crawler-based solutions have become available on the marketplace, however, these solutions are limited in their capabilities and still require significant manual intervention and set-up for each application. Additionally, GPS or prior knowledge of their surroundings which are critical to their operation are often unavailable in an active work environment. An autonomous, self-localizing system would provide significant benefits in these situations, but certain challenges arise from limited situational awareness and poor positional accuracy. The accuracy and robustness of the novel method were assessed and experimentally validated through ground truth readings from a Vicon motion capture system. The localization algorithm’s ability to function on different materials and under various lighting conditions was also explored. Using the example of the receipt inspection of steel plate under 240 lux lighting, the system proved capable of positioning the crawler at the desired position within 5.7 mm.

Investigation on magnetic shield thickness of remote field eddy current probes for inspection of ferromagnetic and non-ferromagnetic plates

Since Remote field eddy current (RFEC) detection technology is barely limited by the skin effect, it can realize the defect detection of large-thickness components. Whereas, the structure of the RFEC probe for the planar conductor is complicated, resulting in an oversized probe. This hinders the application and development of the RFEC testing technology. In this paper, finite element simulations and experimental verification are used to optimize the RFEC probe structure for flat conductors. The results have shown that the thickness of the magnetic shield is the dominant factor in achieving high performance of RFEC inspection of flat conductors. When inspecting both ferromagnetic and non-ferromagnetic flat conductors, cylindrical magnetic shields with appropriate thickness and appropriate excitation frequencies are preferred for improvement of RFEC testing.

Acoustic Structural Integrity Assessment of Ceramics using Supervised Machine Learning and Uncertainty-Based Rejection

Industry and Quality 4.0 pose the opportunity to integrate artificial intelligence-based technology into the quality management of products/services. Particularly, quality control procedures of tableware ceramics require a demanding and faulty human manual (visual and acoustic) inspection. In this paper, we propose an uncertainty-aware automated acoustic inspection using a supervised machine learning model based on a set of novel acoustic features to classify ceramic plates, as cracked and uncracked. We conducted experiments on a dataset of 31 ceramic plates (16 cracked and 15 uncracked), collected in the laboratory. Data quality check and augmentation strategies were also performed, resulting in 2900 samples. The main contributions of this paper are: 1) description of 192 features selected for the acoustic inspection of ceramic plates; 2) comparison of model calibration results regarding three different classifiers; 3) study of different sources of uncertainty for classification with rejection option, through uncertainty quantification measures, and the effect of feature selection on it. We performed two experiments that differ in the usage of a supervised feature selection method. We split the augmented dataset into train/test sets in a proportion of 90/10. The calibrated SVM was selected as the best classifier based on model calibration and cross-validation results and was used in the prediction on the test set. The uncertainty-based rejection improved the train and test sets' classification results. In the experiment with feature selection, the classification performance remained high, while the uncertainty about the predictions and the percentage of rejected samples decreased.

Online optical inspection of electrolytic copper plate based on digital moiré

The application of automatic inspection plays an important role in improving the quality of industrial products. Aiming at nodules on the surface of electrolytic copper plate, this article introduces a comprehensive optical online inspection method. In order to obtain dynamic information of product and overcome the interference of random factors such as large-curvature deformation, position deviation and tilt, the system integrates a variety of single technologies including digital moiré, image recognition and spatial transformation. The online test results show that the system can effectively extract the information of nodules and automatically classify the products according to the set threshold.

Robotic Manufacturing System for Unattended Machining and Inspection of Graphite Bipolar Flow Field Plates for Proton Exchange Membrane Fuel Cells

A single robot-based manufacturing system for unattended machining and inspection of graphite bipolar flow field plates for proton exchange membrane fuel cells is designed and integrated for demonstration and validation. Unlike most robotic manufacturing systems where an industrial robot is used for tending an automated tool such as a computer numerical control machine, in the present system the industrial robot performs all manufacturing operations, including machining the flow fields on both sides of the plates, changing the tools, handling the plates, vacuuming the plates and the workholding device of graphite dust, flipping the plates, air blowing them and performing machine vision inspection for quality control. The toolpath for robotic machining the flow fields and the manifolds are generated offline using Roboguide simulation software. The manufacturing system uses an integrated machine vision inspection process as a diagnostic tool for in-line checking the presence of machined features and in-line verification of feature dimensions. Besides the considerably lower capital cost compared to other automated manufacturing systems resulted from the elimination of the automated machine tool, the proposed robotic cell has the advantage of better managing the abrasive graphite dust resulted in the manufacturing process. The limitations of the proposed robotic cell are assessed and recommendations for further development are considered. The manufacturing system is demonstrated as part of a larger endeavour of bringing to readiness advanced manufacturing technologies for renewable energy devices and responds the high priority needs identified by the U.S. Department of Energy for fuel cells manufacturing research and development.

Evaluation of disbonds in adhesively bonded multilayer plates through local wavenumber estimation

Multilayer plates are widely used as structural components, especially in high-tech industries. However, compared to much simpler homogeneous plates, they pose unique challenges to nondestructive testing (NDT) techniques. The inspections of multilayer plates using traditional ultrasonic (US) testing modalities can be challenging and time-consuming. Faster inspection can be achieved with the use of full-field techniques based on guided elastic waves. In particular, the local wavenumber estimation (LWE) of Lamb waves has been proven effective for damage detection in various plate-like structures. However, the effectiveness of LWE depends on multiple parameters, such as the excitation frequency, the choice of a Lamb wave mode, or the inspection side, among others. This is especially significant in the case of multilayer plates, where different types of damage result in a nontrivial behavior of Lamb waves. In this work, we present a framework for the evaluation of disbonds in adhesively bonded multilayer plates through local wavenumber estimation. The efficacy of the proposed approach is demonstrated on a test sample made of three layers of aluminum with different thicknesses bonded together with an epoxy adhesive. The sample contains artificial defects of various sizes introduced at different adhesive interfaces. First, we present a theoretical analysis of the dispersion curves in the test sample to determine the optimal LWE inspection parameters for particular types of defects. Next, the approach is validated experimentally based on a set of full-wavefield datasets acquired with a scanning laser Doppler vibrometer (SLDV). We demonstrate that the proposed methodology allows obtaining clear images of defects and determining their through-thickness locations in multilayer structures. Based on the obtained results, we believe that the proposed framework can also be effectively applied to different structures and damage types.

Imaging of Increasing Damage in Steel Plates Using Lamb Waves and Ultrasound Computed Tomography.

This paper concerns the inspection of steel plates, with particular emphasis on the assessment of increasing damage. Non-destructive tests were performed on four plates, one of which was undamaged, while the remaining three had defects in the form of circular holes with diameters of 2, 5 and 10 cm. Guided Lamb waves were used in the research, and the image reconstruction was performed using ultrasound computed tomography. The damage size was estimated by tracking the real course of rays and densifying the pixel grid into which the object was divided. The results showed the great potential of ultrasound tomography in detecting defects in steel elements, together with the possibility of estimating damage size.

New open clusters found by manual mining of data based on Gaia DR2

The physical nature of a series of 20 new open clusters is confirmed employing existing data on putative star members, mainly from the second Gaia Data Release (DR2). The clusters were discovered as overdensities of stars by visual inspection of either photographic DSS plates or proper motion plots of random source fields. The reported objects are not present in the most comprehensive or recent catalogs of stellar clusters and associations. For all of them, clumps of comoving stars are revealed in the proper motion space. The parallaxes of the clumped stars are compatible with the real existence of open clusters over narrow ranges of distances. Surface density calculations, free of most noise from non-member sources, allow differentiating a cluster core and an extended cluster corona in some instances. Color-magnitude diagrams generally show a definite main sequence that allows confirmation of the physical existence of the clusters and some of their characteristics. Two of the new clusters seem to form a double system with a common origin. Several of the new clusters challenge the claim of near completeness of the known OC population in the distance range from 1.0 to 1.8 kpc from the Sun (Kharchenko et al.).

Reconstruction of the plantar plate of the metatarsophalangeal joint using adorsal transarticular approach

Reconstruction of the plantar plate to stabilize adislocated or instable lesser metatarsophalangeal joint using adorsal approach in combination with aWeil osteotomy. Dislocated or instable lesser metatarsophalangeal joint with rupture of the plantar plate. Infection, circulatory disorders, symptomatic degenerative arthritis lesser metatarsophalangeal joint. Weil osteotomy using adorsal approach. Temporary dislocation of the metatarsal head as proximal as possible. Inspection of the plantar plate. Assessment and classification of type and extent of the rupture. Suturing of the plantar plate to the plantar bases of the proximal phalanx. Fixation of the Weil osteotomy with correction of the metatarsal alignment. Weight bearing in apostoperative shoe as tolerated. X‑ray control 6weeks postoperative. Full weight bearing in aconventional shoe after bony consolidation. A total of 23surgical reconstructions of the plantar plate (complete plantar plate repair) between 12/2012 and 10/2014 were performed. The mean follow-up was 18.6 (12-30) months. Five secondary dislocations were observed: onedeep postoperative infection, one early dislocation of unknown cause, one secondary dislocation caused by severe hallux valgus recurrence, onemassive foreign body reaction to the non-resorbable sutures, and one late secondary dislocation occurred between 6weeks and 1year postoperative. Normal function of the reconstructed joint was achieved in 13 of the 23reconstructions (57%). Areduced toe purchase was observed in 3reconstructions (13%). Afloating-toe resulted after 7reconstructions (30%).