Non-destructive Measurement Technique Research Articles

Piezoelectric-enhanced ultrasensitive ZnO/Ag microcavity SERS substrate for dopamine detection

In the complex human body fluids, neurotransmitters such as dopamine (DA) play a significant role in regulating physiological functions, thereby impacting the normal functioning of biological activities. Hence, employing a rapid and non-destructive analytical technique for precise measurement of these molecules is crucial. Here, a novel piezoelectric-enhanced ZnO/Ag microcavity SERS substrate was designed, enabling ultra-sensitive and instantaneous detection of DA molecules. This substrate achieves exceptional trace detection performance, with a detection limit for Rhodamine 6 G (R6G) molecules as low as 5×10−13 M and a EF of 1.05×1011, DA can be accurately identified even at exceedingly low concentrations, demonstrating outstanding capabilities in trace detection. This innovative structure not only represents a breakthrough in the rapid trace detection of neurotransmitters but also introduces new avenues for research and diagnostics in fields such as precision medicine, neuroscience, and pharmaceutical development.

Electrical impedance characterization and modelling of Ti-Β implants.

Commercially pure titanium (c.p. Ti) and Ti6Al4V alloys are the most widely used metallic biomaterials in the biomedical sector. However, their high rigidity and the controversial toxicity of their alloying elements often compromise their clinical success. The use of porous β-Titanium alloys is proposed as a solution to these issues. In this regard, it is necessary to implement economic, repetitive, and non-destructive measurement techniques that allow for the semi-quantitative evaluation of the chemical nature of the implant, its microstructural characteristics, and/or surface changes. This study proposes the use of simple measurement protocols based on electrical impedance measurements, correlating them with the porosity inherent to processing conditions (pressure and temperature), as well as the chemical composition of the implant. Results revealed a clear direct relationship between porosity and electrical impedance. The percentage and/or size of the porosity decrease with an increase in compaction pressure and temperature. Moreover, there is a notable influence of the frequency used in the measurements obtained. Additionally, the sensitivity of this measurement technique has enabled the evaluation of differences in chemical composition and the detection of intermetallics in the implants. For the first time in the literature, this research establishes relationships between stiffness and electrical impedance, using approximations and models for the observed trends. All the results obtained corroborate the appropriateness of the technique to achieve the real-time characterization of Titanium implants, in an efficient and non-invasive way.

Rapid single flax (Linum usitatissimum) seed phenotyping of oil and other quality traits using single kernel near infrared spectroscopy

AbstractThe growing interest in the rapid measurement of seed ingredients using single‐kernel NIR (SKNIR) spectroscopy as a nondestructive measurement technique allows fast analysis of sample seed variance that can have effects on breeding and end‐use processing. Flax (Linum usitatissimum), an oilseed crop grown in the Northwest United States and worldwide, is highly beneficial for human health, food, and fiber. Its health benefits include its high protein and omega‐3 fatty acids content. Therefore, seed composition profiles are an important aspect of breeding. The goals of this research were the development of single seed NIR calibration models for protein, oil, and weight of intact flax seeds. In this study, SKNIR spectroscopy was used on a diverse set of flax accessions comprising of 306 samples to create prediction models on a custom built SKNIR instrument. Spectra data and reference protein, oil, and weight were used to build partial least squares (PLS) models. Calibration models provided reasonable prediction of these traits and could be used for screening purposes. PLS statistics were oil (R2 = 0.82, SEP = 1.72), weight (R2 = 0.74, SEP = 0.71), and protein (R2 = 0.62, SEP = 0.96) for validation data sets comprising of one‐third of the total samples. In conclusion, prediction models showed that SKNIR spectroscopy could be a very beneficial nondestructive technique to determine oil and weight as well as rapid screening of protein in single flax seeds while not requiring extensive preparation as compared to traditional techniques.

Nondestructive Measurement Technique for Substandard Amoxicillin Based on Thermal Approach.

In this study, we introduce a new nondestructive measurement technique based on a thermal approach for the determination of substandard amoxicillin. The quality control of amoxicillin is critical for patient safety, and one of the essential parameters for its evaluation is the content of the active ingredient. Traditional methods for assessing amoxicillin content are defined by their time-consuming nature, reliance on skilled personnel, and frequent necessity for specific reagents. The proposed device aims to provide a rapid and low-cost alternative that can accurately measure the amoxicillin content without damaging the sample. The method validation results indicate coefficient of determination (R2) exceeding 0.99, with percent recoveries falling within the range of 98.70-103.40%. The calculated values for limit of detection and limit of quantitation were determined to be 28.11 and 85.17 mg/L, respectively. Our experiments employed amoxicillin samples with predetermined concentrations, all of which were below the standard quality. It was observed that the proposed analytical device effectively quantifies the amoxicillin content in aqueous solutions. Each measurement took no more than 10 min, underscoring the efficiency of the analysis process. The experiments were validated through independent testing at the Government Pharmaceutical Organization in Thailand and the department of engineering science in Oxford, which provides strong evidence for the effectiveness and robustness of the technique. Overall, this study demonstrates the feasibility of using a thermal approach for the nondestructive measurement of substandard amoxicillin.

Machine learning based sinogram interpolation for X-ray computed tomography validated on experimental data

The data driven industry 4.0 and increasing mass-customization of additive manufacturing products require a flexible and high-throughput integration of a 100% quality inspection. While XCT has shown to be a viable non-destructive testing and measurement technique, the long acquisition times remain a stumbling block for a cost-efficient integration in production environments. To mitigate the reduced reconstruction quality from fast acquisition protocols a new sinogram interpolation algorithm is proposed. First, the method re-samples the acquired sinogram data to a new data representation which is called a spinogram. Second, the alternative data format is used to predict the missing X-ray projections using a deep learning network. We implemented this method for real XCT scans and validated on a dataset of five laser sintered objects. When applying the proposed method before the reconstruction step, less noise is present in the reconstructed and segmented volumes. This improves the feature analysis of the measured objects while preserving a lower acquisition time, hence extending the use of XCT towards fast quality inspections.

Residual life prediction of multi-layer composite coatings based on vector quantised and attention-variational AutoEncoder network

ABSTRACT The quality of multi-layer composite coatings (MLCC) directly affects the reliability of equipment. Ultrasonic C-scan has advantages of non-destructive and high-precision. The accurate determination of residual life (RL) from C-scan images of coatings is hindered by the difficulty of feature extraction. To address this issue, a RL prediction method for MLCC based on the Vector Quantised and Attention-Variational AutoEncoder (VQA-VAE) network is proposed. First, by incorporating attention mechanism and residual module, the VQA-VAE network can perform unsupervised feature extraction on C-scan images effectively. Then, the features are fused by two-dimensional entropy weighting method to get the health index (HI), which can be used to establish the RL prediction model. To verify the effectiveness of this method, C-scan is performed on MLCC by ultrasonic microscope to obtain C-scan images, and RL of MLCC is predicted. The experiment results demonstrate that this method achieves higher accuracy in predicting RL of MLCC. In a word, this is a non-destructive, high-efficiency, high-precision, and large-scale measurement technique for MLCC.

Identification Method for XRF Spectral Analysis Based on an AGA-BP-Attention Neural Network

X-ray fluorescence (XRF) spectroscopy is a non-destructive differential measurement technique widely utilized in elemental analysis. However, due to its inherent high non-linearity and noise issues, it is challenging for XRF spectral analysis to achieve high levels of accuracy. In response to these challenges, this paper proposes a method for XRF spectral analysis that integrates an adaptive genetic algorithm with a backpropagation neural network, enhanced by an attention mechanism, termed as the AGA-BP-Attention method. By leveraging the robust feature extraction capabilities of the neural network and the ability of the attention mechanism to focus on significant features, spectral features are extracted for elemental identification. The adaptive genetic algorithm is subsequently employed to optimize the parameters of the BP neural network, such as weights and thresholds, which enhances the model’s accuracy and stability. The experimental results demonstrate that, compared to traditional BP neural networks, the AGA-BP-Attention network can more effectively address the non-linearity and noise issues of XRF spectral signals. In XRF spectral analysis of air pollutant samples, it achieved superior prediction accuracy, effectively suppressing the impact of background noise on spectral element recognition.

An Anti-Noise-Designed Residual Phase Unwrapping Neural Network for Digital Speckle Pattern Interferometry

DSPI (Digital Speckle Pattern Interferometry) is a non-destructive optical measurement technique that obtains phase information of an object through phase unwrapping. Traditional phase unwrapping algorithms depend on the quality of the images, which demands preprocessing such as filtering and denoising. Moreover, the unwrapping time is highly influenced by the size of the images. In this study, we proposed a new deep learning-based phase unwrapping algorithm combining the residual network and U-Net network. Additionally, we incorporated an improved SSIM function as the loss function based on camera characteristics. The experimental results demonstrated that the proposed method achieved higher quality in highly noisy phase unwrapping maps compared to traditional algorithms, with SSIM values consistently above 0.98. In addition, we applied image stitching to the network to process maps of various sizes and the unwrapping time remained around 1 s even for larger images. In conclusion, our proposed network is able to achieve efficient and accurate phase unwrapping.

Effect of water re-curing on the physico-mechanical and microstructural properties of self-compacting concrete reinforced with steel fibers after exposure to high temperatures

The present work aims to investigate the influence of water re-curing- on the physico-mechanical properties and the microstructural of heat-damaged steel fiber-reinforced self-compacting concrete. The experimental approach involves subjecting self-compacting concrete specimens, with and without steel fiber reinforcement, to various high temperatures (400, 600, and 800 °C), with a heating rate of 5 °C/minute. After cooling to ambient temperature, these samples were subjected to water re-curing for 30 days. Non-destructive measurement techniques, such as those of the ultrasonic pulse velocity and dynamic modulus of elasticity, and destructive measurement techniques, such as those of the flexural strength and compressive strength, were performed to assess the level of thermal damage caused to specimens as well as the degree of recovery. Moreover, scanning electron microscope (SEM) analyses were also performed to examine the morphological changes that occurred in concrete and at the fiber/concrete interface. The results obtained showed that the water re-curing process has remarkable effects on the recovery of the microstructure of thermally damaged specimens and its physical and mechanical properties as well. The recovery rate was more important for temperatures above 400 °C. Furthermore, it was found that self-compacting concretes reinforced with steel fibers recurred after exposure to 400 °C and 600 °C exhibited better behavior than self-compacting concrete without fibers, particularly in terms of mechanical resistance.

Surface response regression and machine learning techniques to predict the characteristics of pervious concrete using non-destructive measurement: Ultrasonic pulse velocity and electrical resistivity

It is crucial to assess the characteristics of pervious concrete even post-construction. The quality monitoring of such a procedure is tricky in pervious concrete that it is typically avoided. As a potential means of enhancing the aforementioned quality control, the current study investigates the possibility of predicting characteristics of pervious concrete through response surface methodology and machine learning techniques using non-destructive test measurement (ultrasonic velocity and electrical resistivity). A total of 225 datasets from the experimental study were taken for this study. To recognize the best reliable model for predicting characteristics of pervious concrete, response surface methodology up to sixth order polynomial and five different machine learning techniques were used as statistical assessment tools. Using both ultrasonic pulse velocity and electrical resistivity as predictors for estimating porosity and compressive strength via response surface methodology, using a quadratic model for porosity prediction and a cubic model for compressive strength prediction are recommended. The machine learning models used in the research exhibited superior performance compared to the response surface methodology. Among the many machine learning models evaluated in this study, boosted decision tree regression model better predicted porosity (R2 = 0.92) and compressive strength (R2 = 0.92) of pervious concrete. Therefore, prediction models for the characteristics of pervious concrete are created using non-destructive measurement and machine learning techniques, which may ensure that the construction sector can utilize the offered models without any theoretical expertise.

CNN-GRU based method for peak location of reflected Terahertz signals from thermal barrier coatings

ABSTRACT Due to the limitation of spraying conditions, the microstructure of Thermal Barrier Coatings (TBCs) is not homogeneous, so it is important to extract the peak information of the reflected signal efficiently and accurately using Terahertz (THz) non-destructive detection technique for online thickness measurement. To this end, a hybrid model peak information extraction method based on a convolutional neural network (CNN) and a gated recurrent unit (GRU) is proposed. Firstly, a theoretical model of the THz signal is used to generate the simulated signal; secondly, a 1D-CNN is used to extract features adaptively from the temporal signal input, and a GRU is constructed to learn the temporal information between feature vectors to complete the extraction of peak information; finally, a calibration strategy is employed to determine the highest value around the original localisation result in order to eliminate localisation error and achieve peak location. The proposed CNN-GRU model predicts evaluation metrics that are 2–4 times smaller than those of other methods, and the running time is reduced to 45.71% compared to networks with close prediction accuracy. The method can reduce manual involvement and time expenses while maintaining prediction accuracy and that it provides a new intelligent way for online thickness measuring.

Compact RF transmission unit for nuclear quadrupole resonance spectroscopy

Nuclear quadrupole resonance (NQR) spectroscopy is a non-destructive measurement technique that enables to detect materials of interest very accurately. This technique works by sending high power electromagnetic waves with radio frequency (RF) very specific to the molecular structure to excite the sample and detecting the respond signal with the same frequency. Portable NQR detectors can be potentially developed to detect explosives with non-metallic containers in the fields. However, commercial RF high power amplifiers are large and heavy, so they are not practical for field usage. In this work, we demonstrate a compact NQR transmission part which composed of an active input signal conditioner, a class-D amplifier, and an NQR coil. The active input signal conditioner circuit will convert a single rf pulse with low amplitude (<0.5Vp) to two pulses (with 180° phase difference) and they are subsequently amplified to TTL level. In the class-D amplifier, the pre-amplified pulses are further amplified by a high voltage gate driver IC to 24V amplitude. These high voltage pulses are converted to a single 24V pulse by MOSFETs connected in a half bridge configuration. Finally, the amplified pulse is fed to the NQR coil, i.e., a low impedance LC series resonance circuit. The voltage across the coil at resonance frequency is about 1kVpp.

Micro-infrared thermometry for characterizing microscale heating devices

Infrared thermometry is a non-destructive and non-contact temperature measurement technique that allows for real-time, full-field imaging. However, the limited spatial resolution of infrared detectors and the scarcity of mid- and far-infrared lenses have hindered its use in microscale applications. To address this, we introduce an infrared microscope that employs a bolometer-type infrared detector and a reflective objective lens, enabling high-resolution temperature measurements of microscale heating devices. The reflective objective lens used in the microscope introduces intensity reduction due to magnification and undesirable background radiation caused by its cavity effect. To mitigate these issues, we utilized a custom-built micro-blackbody for calibrating intensity reduction and eliminating background radiation. The proposed micro-infrared thermometry setup was characterized by MDTD and MRTD measurements, as well as an MTF curve with a pixel resolution of 6.7μm/pixel and a spatial resolution of 12.5μm. To evaluate the effectiveness of the micro-infrared thermometry, we fabricated a suspended microscale heating device that can exhibit a clear temperature distribution. The temperature distribution obtained using both the differential conversion and Fast Fourier-Transform methods to eliminate background radiation agreed well, with a maximum difference of 3.2°C and 4.1°C in the range from room temperature to 77°C and 145°C, respectively. Furthermore, we compared our experimental results with COMSOL simulations and found them to be reasonably consistent, with a maximum difference of 6.1°C and 7.0°C in the range from room temperature to 77°C and 145°C, respectively.

Dual-Recognition Triggered Proximity Ligation Combined with a Rolling Circle Amplification Strategy for Analysis of Exosomal Protein-Specific Glycosylation.

Exosomal surface glycan reveals the biological function and molecular information on the protein, especially in indicating the pathogenesis of certain diseases through monitoring of specific protein glycosylation accurately. However, in situ and nondestructive measurement techniques for certain Exosomal glycoproteins are still lacking. In this work, combined with on-chip purification, we designed a proximity ligation assay-induced rolling circle amplification (RCA) strategy for highly sensitive identification of Exosomal protein-specific glycosylation based on a couple of proximity probes to target Exosomal protein and the protein-specific glycosylation site. Benefiting from efficient separation, scalable dual-recognition, and proximity-triggered RCA amplification, the proposed strategy could convert different protein-specific glycan levels to prominent changes in absorbance signals, resulting in accurate quantification of specific glycosylated Exosomal protein. When detecting the glycosylated PD-L1 on MDA-MB-231 exosomes and glycosylated PTK7 on HepG2 exosomes, the detection limits were calculated to be as low as 1.04 × 104 and 2.759 × 103 particles/mL, respectively. In addition, we further expand the dual-recognition site to investigate the potential correlation of Exosomal glycosylation with polarization of THP-1 cells toward the tumor-suppressive M1 phenotype. Overall, this strategy provides a universal tool for multiple analyses of diverse protein-specific glycosylated exosomes, exhibiting enormous potential to explore exosome function and search for new early diagnosis markers.

A non-destructive electro-acoustic method to characterize the pull-in voltage of electrostatic actuators

For electrostatic actuators, the pull-in marks an upper limit for the operation range. Once reached, the electrodes come into contact and are shorted without further protection. A non-destructive measurement technique to predict this failure mode is of high interest to allow, e.g. fabrication monitoring or reliability studies. To this end, we develop a surprisingly simple nonlinear lumped parameter model (LPM) for a rather complex electrostatic actuator, designed for an in-ear loudspeaker application. It turns out that a single degree-of-freedom model with only one parameter is sufficient. Our key approach is to experimentally determine this free model parameter by analysing harmonic distortions at low frequencies. Harmonic distortions are a very sensitive tool for nonlinearities. Our method is suggested by simulations with a 2D stationary finite element method (FEM), demonstrating how the analysis of harmonic distortions for voltages far below the pull-in can predict not only the DC pull-in but also the quasi-static AC pull-in voltages at different working points. The distortion analysis of electrostatic actuator ensembles therefore seems a viable route for their non-destructive characterization in the nonlinear domain.

A Short Review of Through-Silicon via (TSV) Interconnects: Metrology and Analysis

This review investigates the measurement methods employed to assess the geometry and electrical properties of through-silicon vias (TSVs) and examines the reliability issues associated with TSVs in 3D integrated circuits (ICs). Presently, measurements of TSVs primarily focus on their geometry, filling defects, and the integrity of the insulating dielectric liner. Non-destructive measurement techniques for TSV contours and copper fillings have emerged as a significant area of research. This review discusses the non-destructive measurement of contours using high-frequency signal analysis methods, which aid in determining the stress distribution and reliability risks of TSVs. Additionally, a non-destructive thermal detection method is presented for identifying copper fillings in TSVs. This method exploits the distinct external characteristics exhibited by intact and defective TSVs under thermoelectric coupling excitation. The reliability risks associated with TSVs in service primarily arise from copper contamination, thermal fields in 3D-ICs, stress fields, noise coupling between TSVs, and the interactions among multiple physical fields. These reliability risks impose stringent requirements on the design of 3D-ICs featuring TSVs. It is necessary to electrically characterize the influence of copper contamination resulting from the TSV filling process on the reliability of 3D-ICs over time. Furthermore, the assessment of stress distribution in TSVs necessitates a combination of micro-Raman spectroscopy and finite element simulations. To mitigate cross-coupling effects between TSVs, the insertion of a shield between them is proposed. For efficient optimization of shield placement at the chip level, the geometric model of TSV cross-coupling requires continuous refinement for finite element calculations. Numerical simulations based on finite element methods, artificial intelligence, and machine learning have been applied in this field. Nonetheless, comprehensive design tools and methods in this domain are still lacking. Moreover, the increasing integration of 3D-ICs poses challenges to the manufacturing process of TSVs.

Non-Destructive Measurements for 3D Modeling and Monitoring of Large Buildings Using Terrestrial Laser Scanning and Unmanned Aerial Systems

Along with the development and improvement of measuring technologies and techniques in recent times, new methods have appeared to model and monitor the behavior of land and constructions over time. The main purpose of this research was to develop a new methodology to model and monitor large buildings in a non-invasive way. The methods proposed in this research are non-destructive and can be used to monitor the behavior of buildings over time. A method of comparing point clouds obtained using terrestrial laser scanning combined with aerial photogrammetric methods was used in this study. The advantages and disadvantages of using non-destructive measurement techniques over the classic methods were also analyzed. With a building located in the University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca campus as a case study and with the help of the proposed methods, the deformations over time of the facades of that building were determined. As one of the main conclusions of this case study, it can be stated that the proposed methods are adequate to model and monitor the behavior of constructions over time, ensuring a satisfactory degree of precision and accuracy. The methodology can be successfully applied to other similar projects.

Spatial imaging of stratified heterogeneous microstructures: determination of the hardness penetration depth in thermally treated steel parts by laser ultrasound

We introduce an improved non-destructive and non-contact measurement technique for the hardness penetration depth of thermally hardened steel parts based on all-optical laser ultrasound. The hardening of the near-surface region results in a stratified system with layers of different microstructural characteristics caused by changes in grain size. We employ spatially resolved ultrasonic backscattering signals to provide subsurface imaging of the axial and lateral hardness profile. Spatial scans over a range of 30 mm with a lateral step size of 100 μm were obtained within 30 s. We investigated three common steel grades with different microstructural characteristics and hardened layer thicknesses ranging from about 3 to 10 mm. The hardened surface layer and the unhardened core material can be identified. The data evaluation is assisted by a supervised machine learning approach that takes advantage of the spatio-temporal ultrasonic backscattering information. This method also provides an alternative route to define the hardness penetration depth using spatio-temporal scattering characteristics.

Utility of Ultrasonic Pulse Velocity for Estimating the Overall Mechanical Behavior of Recycled Aggregate Self-Compacting Concrete

Ultrasonic pulse velocity (UPV) is a non-destructive measurement technique with which the quality of any concrete element can be evaluated. It provides information on concrete health and for assessing the need for repair in a straightforward manner. In this paper, the relationship is studied between UPV readings and the mechanical behavior of self-compacting concrete (SCC) containing coarse, fine, and/or powdery RA. To do so, correlations and simple- and multiple-regression relationships between compressive strength, modulus of elasticity, splitting tensile strength, flexural strength, and UPV readings of nine SCC mixes were assessed. The correlations showed that the relationship of UPV with any mechanical property was fundamentally monotonic. The inverse square-root model was therefore the best-fitting simple-regression model for all the mechanical properties, although for bending-tensile-behavior-related properties (splitting tensile strength and flexural strength) the estimation accuracy was much lower than for compressive-behavior-related properties (compressive strength and modulus of elasticity). Linear-combination multiple-regression models showed that the properties related to bending-tensile behavior had a minimal influence on the UPV value, and that their introduction resulted in a decreased estimation accuracy. Thus, the multiple-regression models with the best fits were those that linked the compressive-behavior-related properties to the UPV readings. This therefore enables the estimation of the modulus of elasticity when the UPV and compressive strength are known with a deviation of less than ±20% in 87% of the SCC mixes reported in other studies available in the literature.

The effect of thermal aging degradation of CFRP composite on its mechanical properties using destructive and non-destructive methods and the DIC system

This paper presents the results of laboratory tests for CFRP (Carbon Fiber Reinforced Polymer) composite subjected to aging in different conditions: temperature (250 °C and 300 °C) and time (60, 65, 75 and 90 min) to assess its thermal degradation and mechanical response. The tests were conducted for six batches of five samples each. The last batch was treated as reference and was not subjected to aging. To evaluate the effect of thermal degradation both nondestructive and destructive measurement techniques were used to study changes in mechanical properties including damage growth. The nondestructive ODS (Operational Deflection Shapes) technique concerned estimation of natural frequency measurements for cantilever beams, while the destructive technique involved uniaxial tensile tests. In both cases, the whole deformation and degradation processes were monitored by the Aramis system using the Digital Image Correlation (DIC) technique. The results show that despite changes in Young's modulus of aging specimens and their thickness, the natural frequency remains constant for the first and second modes. However, for specimens with higher levels of thermal aging degradation two additional modes of natural vibration were observed, i.e. the third and fourth. The aging process caused a 21% decrease in Young's modulus, an increase in Poisson's ratio by 340%, and finally a reduction in the strength of the CFRP composite by 34%.