Nondestructive Evaluation Approach Research Articles

Novel baseline-free ultrasonic Lamb wave defect location method based on path amplitude matching

Ultrasonic Lamb wave detection technology constitutes a non-destructive evaluation approach extensively employed for the identification of flaws within plate-like structures. The conventional method for detecting and localizing defects in isotropic plate-like structures using ultrasonic Lamb waves relies on baseline signal data. However, the reliability of baseline data as a reference value is diminished due to varying working conditions of the structure. Therefore, to overcome the influence of mismatched baseline data, this paper proposes a novel non-baseline Lamb wave defect detection and localization method. Through simulation and experiment studies, it is discovered that defects at different positions have varied impacts on the amplitude of direct wave-packets under the same propagation path. By eliminating differences in the piezoelectric excitation characteristics of the sensing array (normalized through boundary reflect wave), the direct wave amplitude of multiple sensor pairs in the circular array can be compared and ranked. The paths closest to the location of the damage can be identified, enabling to obtain the defect location information. In this paper, the feasibility and effectiveness of this method has been verified by simulation and practical experiments. The experimental data and imaging results obtained over a four-month period demonstrate that, compared to the traditional baseline localization method, the baseline-free method proposed in this study exhibits a greater ability to resist interference caused by changes in environmental temperature. By increasing the number of sensors from 16 to 32, the positioning accuracy can be significantly improved, reducing the positioning deviation from 13 mm to 0.42 mm. This new non-baseline method based on path amplitude matching demonstrates enhanced practicality within the realm of engineering. Notably, this method holds the potential to be synergistically incorporated and applied in conjunction with various other measurement techniques.

Nonlinear autoregression-based non-destructive evaluation approach for railway tracks using an ultrasonic fiber bragg grating array

The safe operation of railway systems relies on rapid and accurate non-destructive damage detection and evaluation techniques to help rule out potential threats brought by rail track damages. In this paper, an innovative fiber Bragg grating (FBG) array-based sensing system is developed for rail defect inspection by integrating the ultrasonic testing technology, and a nonlinear autoregressive neural network with exogenous inputs (NARX) is proposed for rail damage detection based on measured ultrasonic signals. The developed sensing system can realize the high-speed and multiplexed ultrasonic signals demodulation. The NARX is proposed to process the ultrasonic signals obtained by the developed sensing system, and a specified probabilistic damage sensitive feature (DSF) is tailored to indicate rail health status, derived from the probability density function (PDF) of NARX prediction errors. To verify the proposed sensing system and damage identification algorithm, experimental studies considering four rail conditions (i.e., intact, cracked, bump, and welded conditions) are conducted in this study. First, baseline NARX models are priorly trained using ultrasonic signals acquired on an intact rail segment; then, DSFs corresponding to each condition are calculated enabling effective rail damage diagnosis. To optimize the training parameters of NARX models, three different training functions are utilized to select the most appropriate parameters that are sensitive to rail damages. Last, large-scale testing is conducted, further proving the applicability and reliability of the proposed rail damage evaluation approach. Through this study, it is suggested that integrating high-performance sensing technologies with effective data processing tools would maximumly enable efficient and robust non-destructive testing, especially on important infrastructures such as rails.

Machine learning-enabled thickness estimation of thin coatings on carbon fibre composites using microwaves

Accurate thickness measurement of thin coatings (typically 50–500 μm) on carbon fibre-reinforced polymer composites is a major challenge in the manufacturing and maintenance processes of modern aircraft. Different from the conventional material-dependent technique for prediction, a machine learning-enabled strategy with an artificial neural network configuration is used with no requirement of prior knowledge of the type of coating or substrate under test. In the test, an open microwave cavity resonator sensor is directly placed on a coated composite, and any variation of the coating material, coating thickness and conductivity of the composite alters the resonance frequency. Principal component analysis is employed in the signal pre-processing for the dimensionality reduction of the raw measurement data. In terms of the root-mean-square error, the maximum value for the calibration approach is approximately 15 μm and that for the machine learning-based approach is 12 μm. The sensor system developed enables real-time on-site assessment of coated composite structures and thus offers a new approach for non-destructive evaluation 4.0 with improved efficiency, accuracy and automation.

Depth Resolved Thermal Wave Imaging Approach for Non-Destructive Testing and Evaluation of Steel Sample

Depth Resolved Thermal Wave Imaging Approach for Non-Destructive Testing and Evaluation of Steel Sample

A review of non-destructive evaluation techniques for axial thermal stress and neutral temperature measurement in rail: Physical phenomena and performance assessment

Thermal buckling of continuous welded rail (CWR) has been a long-standing challenge for the railroad industry because of the high derailment rate and the associated social, economic, and environmental impacts it causes. Rail buckling is generally attributed to excessive thermally-induced axial compressive stress developed in the rail from high temperatures. Knowing the rail thermal stress or its rail neutral temperature (RNT) is critical for safe and efficient rail system operation. There has been great interest and much work on the development of nondestructive evaluation (NDE) techniques to estimate rail thermal stress and RNT in situ. This paper reviews the findings and conclusions from research about NDE approaches for estimating rail thermal stress or RNT, emphasizing the physical phenomena and performance interpretation related to each of the approaches. We identify the type of reference measurements each technique relies on and tabulate this information showing key assumptions, performance, and limitations for each technique.

Acoustic emission source localisation for structural health monitoring of rail sections based on a deep learning approach

An acoustic emission (AE) approach for non-destructive evaluation of structures has been developed over the last two decades. In complex structures, one of the limitations of AE testing is to find the location of the AE source. Time of flight and wave velocity are typically employed to localise AE sources. However, complex rail structures generate multiple wave modes travelling at varying speeds, making localisation difficult. In this paper, the challenge of localisation has been split into two parts: (a) identification of the AE source zone, i.e. head, web or foot, and (b) identification of location along the length of the rail. AE events are simulated using a pencil lead break (PLB) as the source. Three models including an artificial neural network and 1D and 2D convolutional neural networks (CNNs) are trained and tested using AE signals generated by PLB sources. The accuracy of zone identification is reported as 94.79% when using the 2DCNN algorithm. For location classification it is also found that 2DCNN performed best with 73.12%, 79.37% and 67.50% accuracy of localising the AE source along the length in the head, web and foot, respectively. For AE signal generation from actual damage in a rail, a bending test on an inverted damaged rail section was then performed with loads of 100 kN, 150 kN and 200 kN. For all loads, the 2DCNN model resulted in accurate prediction of the zone of the AE source, and it accurately predicted the AE source location along the length for the loads of higher intensity (150 kN, 200 kN). It is envisaged that the deep learning approach presented in this research work will be helpful in developing a real-time monitoring system for rail inspection based on AE.

Neutron Imaging of Al6061 Prepared by Solid-State Friction Stir Additive Manufacturing

Solid-state Friction Stir Additive Manufacturing has recently gained attention as a result of its capacity to fabricate large-scale parts while preserving the mechanical properties of the feedstock material. However, the correlation between the quality of layer-by-layer bonding of the deposited metal and processing parameters has remained unknown. Neutron imaging techniques, with 90% total transmission per cm, are employed for Al6061 parts fabricated by MELD® Technology as a non-destructive evaluation approach for the first time to investigate the layer-by-layer structure of a stadium-shaped ingot in different sections. The post-processed results show the fabricated parts with an optimized set of processing parameters are void-free. However, the hydrocarbon-based feedstock lubricant segregates between the layers, which consequently may lead to non-uniform weaker mechanical properties along the build direction and stimulate crack initiation during mechanical loading. The tensile test results show 14% lower strain-to-failure values in alleged contaminated areas in transmission imaging results. Additionally, layer bonding is significantly impacted by hot-on-hot and hot-on-cold layer deposition schemes, especially for larger layer thicknesses.

State Characterization of Lithium-Ion Battery Based on Ultrasonic Guided Wave Scanning

Accurate state characterization of batteries is conducive to ensuring the safety, reliability, and efficiency of their work. In recent years, ultrasonic non-destructive testing technology has been gradually applied to battery state estimation. In this paper, research on the state characterization of lithium-ion batteries based on ultrasonic guided wave (UGW) scanning is carried out. The laser Doppler vibrometer (LDV) and the X-Y stage are used to obtain the surface scanning UGW signal and the line scanning UGW signal of lithium-ion batteries under different states of charge and different aging degrees. The propagation law of UGWs in the battery is analyzed by surface scanning signals, then the energy spectrum of the signals is calculated, showing that the aging of the battery attenuates the transmission energy of UGWs. The “point” parameters are extracted from the scanning point signals. On this basis, the “line” parameters composed of line scanning multi-point signals are extracted. By analyzing the changing law of parameters during the charge–discharge process of batteries, several characteristic parameters that can be used to characterize the battery state of charge and state of health are obtained. The method has good consistency in the state characterization of the three batteries and provides a new approach for non-destructive testing and evaluation of battery states.

A novel low-cost capacitance-based approach for non-destructive evaluation of powder quality for food processing industries

In a high-volume food production line, several stainless-steel hoppers store, mix and dispense powdered ingredients to form food powders of the desired composition. Improper mixing at high speeds and caking (formation of lumps) during storage hamper powder homogeneity and quality. Currently, low throughput offline techniques such as spectroscopic analysis and powder flow tests are used to screen for powder mixing uniformity and caking. Herein, a flexible electrode system is proposed to detect capacitance changes based on the dielectric differences between different powders (beverage powders and their mixtures) and their subsequent variation from poor mixing/caking. The detection scheme successfully differentiated different powder mixtures and detected caking in powders non-invasively with good detection speed (50 ms) and low detection limits (15% for powder mixing and 1–43% for powder caking). Furthermore, the capacitive system integrated with an industrial food hopper successfully showcased an in-situ automated detection of powder caking.

Testing the Dispersion of Nanoparticles in a Nanocomposite with an Ultra-Low Fill Content Using a Novel Non-Destructive Evaluation Technique.

A non-destructive evaluation (NDE) technique capable of testing the dispersion of nanoparticles in a nanocomposite would be of great use to the industry to check the quality of the products made and to ensure compliance with their specifications. Very few NDE techniques found in the literature can evaluate the level of dispersion of the nanoparticles in the whole nanocomposite. Here, a recently developed NDE technique based on pulsed phase thermography (PPT) in transmission mode was used to assess the particle dispersion in ultra-low, less than 0.05 wt%, Ag enriched polymeric based nanocomposite manufactured with an innovative nano-coating fragmentation technique. The phasegrams obtained with the presented technique clearly showed clusters or bundles of Ag nanoparticles when present, down to the size of 6 µm. Therefore, the new NDE approach can be applied to verify that the expected levels of dispersion are met in the production process.

On ultrasound propagation in composite laminates: advances in numerical simulation

The growing use of composite materials for aerospace applications has resulted in the need for quantitative methods to analyze composite components. Ultrasonic guided waves constitute the physical approach for nondestructive evaluation (NDE) and structural health monitoring (SHM) of solid composite materials, such as carbon fiber reinforced polymer (CFRP) laminates. Ultrasound based NDE methods are commonly used in the aerospace field, but ultrasonic wave behavior can be complicated by the presence of material anisotropy, complex geometries (e.g., highly curved parts, stiffeners, and joints) and complex geometry defects. Common defects occurring in aerospace composites include delaminations, porosity, and microcracking. Computational models of ultrasonic wave propagation in CFRP composites can be extremely valuable in designing practical NDE and SHM hardware, software, and methodologies that accomplish the desired accuracy, reliability, efficiency, and coverage. Physics based simulation tools that model ultrasonic wave propagation can aid in the development of optimized inspection methods and in the interpretation of NDE data. This paper presents a review of numerical methodologies for ultrasound and guided wave simulation in fiber reinforced composite laminates summarizing the relevant works to date, different methods, and their respective applications.

Latest Advances in Common Signal Processing of Pulsed Thermography for Enhanced Detectability: A Review

Non-destructive testing (NDT) is a broad group of testing and analysis techniques used in science and industry to evaluate the properties of a material, structure, or system for characteristic defects and discontinuities without causing damage. Recently, infrared thermography is one of the most promising technologies as it can inspect a large area quickly using a non-contact and non-destructive method. Moreover, thermography testing has proved to be a valuable approach for non-destructive testing and evaluation of structural stability of materials. Pulsed thermography is one of the active thermography technologies that utilizes external energy heating. However, due to the non-uniform heating, lateral heat diffusion, environmental noise, and limited parameters of the thermal imaging system, there are some difficulties in detecting and characterizing defects. In order to improve this limitation, various signal processing techniques have been developed through many previous studies. This review presents the latest advances and exhaustive summary of representative signal processing techniques used in pulsed thermography according to physical principles and thermal excitation sources. First, the basic concept of infrared thermography non-destructive testing is introduced. Next, the principle of conventional pulsed thermography and signal processing technologies for non-destructive testing are reviewed. Then, we review advances and recent advances in each signal processing. Finally, the latest research trends are reviewed.

Lock-in thermography using miniature infra-red cameras and integrated actuators for defect identification in composite materials

A novel approach for thermographic Non-Destructive Evaluation (NDE) of laminated polymer composite structures is presented. The technique is based on Lock-In Thermography (LIT), which traditionally uses an external heat source. Here a new means of internal heating, via a lightweight embedded actuator capable of providing highly repeatable and uniform heating is presented. The equipment cost, complexity and hence size is reduced by removing the need for high power external thermal excitation. Instead, the necessary temperature modulation of the internal actuator is achieved with a compact and low-cost Arduino controlled circuit. The size and cost of the equipment is further reduced by demonstrating that a miniature printed circuit board mounted thermal core type micro-bolometer can be used effectively for LIT. The performance of the thermal core is quantitatively compared with the more expensive and bulky traditional infra-red cameras. It is shown that the thermal core can detect defects with similar overall performance as a cooled photon detector and an uncooled micro-bolometer. The low-cost, compact nature and small mass of the thermal core offers great potential in thermographic inspection opening the possibility of deploying devices permanently on structures in conjunction with the embedded actuators for in-situ monitoring in the service environment.

A new approach for predicting board MOE from increment cores

Key message Increment cores can provide improved predictive capabilities of the modulus of elasticity (MOE) of sawn boards. Multiple increment cores collected at different heights in a tree provide marginally increased accuracy over a single breast-height core, with higher labour costs. Approximately 50% of the variability of the static bending MOE of individual boards is explained by the predicted MOE obtained from a single increment core taken at breast height. Context Prediction of individual board MOE can lead to accurate optimisation of the value extracted from forest resources, and enhanced decision-making on the management and allocation of the resource to different processors, and improve the processors ability to optimise grade allocation. Aims The objective of this study is to predict the MOE of individual sawn boards from the MOE measured from cores collected from standing trees. Methods A five-parameter logistic (5PL) function and radial basis function interpolants are used to obtain a continuous distribution of MOE throughout a log. By developing a “virtual sawing” methodology, we predict the individual board MOE for sixty-eight trees consisting of locally developed F1 and F2 hybrid pines (Pinus caribaea var. hondurensis × Pinuselliottii var. elliottii). Results Moderate correlations for individual board predictions are observed, with R2 values ranging from 0.47 to 0.53. Good correlations between average predicted board MOE and average measured MOE are also observed, with R2 ≈ 0.83. A pseudo-three-dimensional approach, accounting for variation in height in the tree, affords marginally greater accuracy and predictive capability at the cost of increased data collection and processing. By using a single breast-height core, we can obtain a similar level of prediction of individual board MOE. Conclusion We have presented a novel non-destructive evaluation approach to predict the MOE of individual boards sawn from trees. This approach can be adapted to other wood properties, and other wood products obtained from trees.

Pulse compression favorable frequency modulated thermal wave imaging for non-destructive testing and evaluation: an analytical study

InfraRed Thermography (IRT) is one of the non-destructive testing and evaluation (NDT&E) approach widely used for testing and evaluation of wide verity of materials such as metals, semiconductors and composites. Among the widely used Thermal NDT&E (TNDT&E) approaches for better depth resolution and sensitivity for detection of defects located at different depths inside the test specimen recently proposed correlation based approach gained importance due to its enhanced defect detection capabilities. The present paper introduces a novel one-dimensional analytical solution for the frequency modulated excitation scheme under adiabatic boundary conditions for detection of flat bottom holes as defects in a mild steel sample. The performance of the Pulse Thermography (PT), Lock-in Thermography (LT) and Frequency Modulated Thermal Wave Imaging (FMTWI) methods are highlighted their defect detection capabilities have been compared by adopting the recently introduced correlation based post-processing approach. Finally, the proposed analytical method has been validated with the results obtained from the commercially available finite element based software.

Autonomous nondestructive evaluation of resistance spot welded joints

The application of non-destructive evaluation approaches has attracted strong interests in modern automotive industries. This study presents an autonomous deep-computing framework to analyze raw videos from infrared systems and to predict weld nugget shape and size with unprecedented accuracy and speed. In a comprehensive training and testing experiment with 90 videos (seven sets of welding material stack-ups), a new method was developed to assemble sufficient datasets for neural network training. Our framework successfully predicts all the nugget shapes with F1 scores that range from 0.84 to 0.92. The total training time on Nvidia DGX station takes less than 10 min for each set of welding material stack-up. The real inference time of an individual dataset (with 30 video frames) takes about 0.005 s. The procedure and methods developed in the study can be applied to other image-based weld property prediction, as well as other manufacturing processes. Furthermore, our well-trained neural networks take limited memory resources (2.3 MB) and are suitable for embedded microprocessors for in-situ welding quality control as edge computing within an intelligent welding framework.

An analytical approach for frequency modulated thermal wave imaging for testing and evaluation of glass fiber reinforced polymers

Frequency modulated thermal wave imaging (FMTWI) has been considered as one of the promising non-destructive testing and evaluation approach due to its merits such as economical, safe, fast, sensitive and high depth resolvability. The present work provides a novel analytical solution for FMTWI using one-dimensional heat conduction equation with adiabatic (Neumann) boundary conditions. The temperature gradient over the glass fiber reinforced polymer specimen has been analyzed and validated with a commercially available three dimensional mathematical finite element model to retrieve the quantitative information regarding the subsurface defects. The efficiency of the proposed method is highlighted using matched filter based approach for a frequency modulated imposed heat flux. The depth resolvability of the proposed method has been studied from the obtained correlation lag and the time domain phase obtained for FMTWI technique.

Study on cracks defect inspection for GFRP laminates using laser excitation chirp pulsed photothermal radar imaging (CP-PRI)

Laser excitation chirp pulsed photothermal radar imaging (CP-PRI), as a novel nondestructive testing and evaluation (NDT&E) approach, is employed to recognize the surface crack at glass fiber reinforced polymer (GFRP) composite materials. The mechanism of crack detection has been demonstrated in detail by a photothermal method and the principle of CP-PRI. A GFRP specimen with artificial surface crack is prepared for NDT&E by CP-PRI. First-order differential processing was employed to enhance edge sharpening of the amplitude image. The effects of the laser power, excitation pulsed width, reference pulsed width, delay time and chirp frequency on the signal-noise-ratio (SNR) of cracks are completely investigated. The results illustrated that the SNR of the crack is increased with the increase of laser power, the opening width of crack and excitation pulse width. The reference pulse width has few implications for the SNRs of the smaller opening width cracks (~100 μm). However, the reference initial delay time and chirp frequency had a significant influence on the SNRs of cracks. The comparison experiments between thermal wave radar imaging (TWRI) and CP-PRI has been carried out, its results indicated that the CP-PRI has the higher SNRs of cracks than that of TWRI. Consequently, CP-PRI is available for the detection of GFRP surface cracks as a result of its high detectability.

Carbon Nanotubes Dispersion Assessment in Nanocomposites by Means of a Pulsed Thermographic Approach.

The extensive production of polymer composites reinforced by carbon nanotube is limited by the absence of non-destructive evaluation (NDE) methods capable of assessing product quality to guarantee compliance with specifications. It is well known that the level of dispersion of carbon nanotubes (CNTs) in the polymer matrix is the parameter that, much more than others, can influence their enhancement capabilities. Here an active Infrared Thermography Non Destructive Testing(IR-NDT) inspection, joined with pulsed phase thermography (PPT), were applied for the first time to epoxy-CNT composites to evaluate the level of dispersion of the nanoparticles. The PPT approach was tested on three groups of epoxy nanocomposite samples with different levels of dispersion of the nanoparticles. The phasegrams obtained with the presented technique clearly show clusters, or bundles, of CNTs when present, so a comparison with the reference sample is not necessary to evaluate the quality of the dispersion. Therefore, the new NDE approach can be applied to verify that the expected dispersion levels are met in products made from epoxy and Multi-Walled Carbon Nanotubes (MWCNTs). The mechanisms underlying the effects of the dispersion of carbon nanotube on the thermal response of polymer composites have been identified.

Evaluation of hygrothermal ageing in CFRP composite material using a non-destructive approach

Hygrothermal (HT) ageing is a major concern for polymer composites in aircraft structures. An aircraft gets inspected after certain flying hours/cycles consuming man-hours, money and time. For economic inspection, non-contact, Non-destructive Evaluation (NDE) techniques can prove to be effective. This study presents NDE approaches using three such techniques, namely, X-ray radiography, ultrasound, and infrared thermography for evaluation of HT-ageing in Carbon Fiber Reinforced Polymer (CFRP) composites. For accelerating the ageing, specimens were subjected to a hot water environment up to more than 1000 hours at [Formula: see text]C. It’s influence on different NDE parameters through each technique was investigated. Building on the findings, the effectiveness of these techniques for the evaluation of HT-ageing in CFRP has been demonstrated.