Nondestructive Detection Technique Research Articles

Superimposed multi-harmonic interference frequency and phase measurements based on non-synchronous sampling quantization and all-phase spectrum correction

As a non-destructive detection technique for measuring parallel transparent optics, wavelength-shifting interferometry provides a separation basis for multiple measured surfaces by assigning different frequencies to the interference harmonics based on their optical-path-difference. However, the non-synchronous sampling and spectral aliasing can decrease the measurement accuracy. Therefore, an approximate entropy algorithm is utilized to determine the sampling performance. Then the frequency search accuracy is guaranteed by the zero-padding method, and the all-phase Fourier transform method is introduced to correct the harmonic frequencies and amplitudes, where the spectral aliasing problem is solved by the relative amplitude analysis. So efficient surface measurement combining the above advantages is realized, and the validity is verified by simulation analysis of different profile features and de-tuning errors. The effectiveness of the proposed method is fully confirmed by the repetitive measurements on two parallel plates in the experiments.

FFF print defect characterization through in-situ electrical resistance monitoring

Fused filament fabrication is a popular fabrication technique. Currently there is a need for in-situ monitoring modalities to gather real-time information on prints, both for quality control and closed-loop control. Despite current advancements, effective and affordable in-situ monitoring techniques for non-destructive defect detection of voids and bonding quality are still limited. This work demonstrates in-situ monitoring of fused filament fabrication through electrical resistance measurements as an alternative to thermal and optical methods. A new, easy-to-implement setup is demonstrated which measures the electrical resistance of a conductively doped filament between the nozzle and single or multi-electrodes on the bed. Defects can be located in an unprecedented way with the use of encoded axes in combination with the observed resistance variations throughout the part. A model of the anisotropic electrical conduction is used to interpret the measurements, which matches well with the data. Warping, inter-layer adhesion, under-extrusion and overhang sagging print defects can be observed in the measurements of parts with a complex geometry, which would be difficult to measure otherwise. Altogether in-situ electrical resistance monitoring offers a tool for optimising prints by online studying the influence of the print parameters for quality assessment and it opens up possibilities for closed-loop control.

Spontaneous-stimulated Raman co-localization dual-modal analysis approach for efficient identification of tumor cells

The study of highly heterogeneous tumor cells, especially acute myeloid leukemia (AML) cells, usually relies on invasive analytical methods such as morphology, immunology, cytogenetics, and molecular biology classification, which are complex and time-consuming to perform. Mortality is high if patients are not diagnosed in a timely manner, so rapid label-free analysis of gene expression and metabolites within single-cell substructures is extremely important for clinical diagnosis and treatment. As a label-free and non-destructive vibrational detection technique, spontaneous Raman scattering provides molecular information across the full spectrum of the cell but lacks rapid imaging localization capabilities. In contrast, stimulated Raman scattering (SRS) provides a high-speed, high-resolution imaging view that can offer real-time subcellular localization assistance for spontaneous Raman spectroscopic detection. In this paper, we combined multi-color SRS microscopy with spontaneous Raman to develop a co-localized Raman imaging and spectral detection system (CRIS) for high-speed chemical imaging and quantitative spectral analysis of subcellular structures. Combined with multivariate statistical analysis methods, CRIS efficiently differentiated AML from normal leukocytes with an accuracy of 98.1 % and revealed the differences in the composition of nuclei and cytoplasm of AML relative to normal leukocytes. Compared to conventional Raman spectroscopy blind sampling without imaging localization, CRIS increased the efficiency of single-cell detection by at least three times. In addition, using the same approach for further identification of AML subtypes M2 and M3, we demonstrated that intracytoplasmic differential expression of proteins is a marker for their rapid and accurate classifying. CRIS analysis methods are expected to pave the way for clinical translation of rapid tumor cell identification.

Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach

Raman spectroscopy is a general and non-destructive detection technique that can obtain detailed information of the chemical structure of materials. In the past, when using chemometric algorithms to analyze the Raman spectra of mixtures, the challenges of complex spectral overlap and noise often limited the accurate identification of components. The emergence of deep learning has introduced a novel approach to qualitative analysis of mixed Raman spectra. In this paper, we propose a deep learning-based Raman spectroscopy qualitative analysis algorithm (RST) by borrowing the ideas of convolutional neural network and Transformer. By transforming the Raman spectrum into 64 word vectors, the contribution weights of each word vector to the components are obtained. For the 75 spectral data used for validation, the positive identification rate can reach 100.00 %, the recall rate can reach 99.3 %, the average identification score can reach 9.51, and it is applicable to the fields of Raman and surface-enhanced Raman spectroscopy. Furthermore, compared with traditional CNN models, RST has excellent accuracy and robustness in identifying components in complex mixtures. The model's interpretability has been enhanced, aiding in a deeper understanding of spectroscopic learning patterns for future analysis of more complex mixtures.

Enhancing Damage Detection in 2D Concrete Plates: A Comprehensive Study on Interpolation Methods and Parameters

In an era marked by increasing demands for stability and durability in construction, the importance of damage detection in concrete structures cannot be overstated. As these structures underpin the safety and longevity of vital assets, this paper embarks on a comprehensive exploration of methodologies to enhance precision and reliability in 2D concrete plate damage detection. By focusing on the interpolation of damage index values and leveraging the insights gained from energy loss analysis and the characterization of the time of arrival of signals, we address the pressing need for improved non-destructive damage detection techniques. Our study encompasses a range of simulation attempts, each involving various interpolation parameters, and systematically evaluates their performance. The culmination of this research identifies the most effective combination of techniques and parameters, leading to the best results in damage detection. This multidimensional investigation promises to provide valuable contributions to the field of structural health monitoring, benefiting both researchers and practitioners engaged in the evaluation of concrete structures.

Vis/NIR reflectance spectroscopy for non-destructive diagnosis of Fusarium spp. infection in postharvest potato tubers (Solanum tuberosum)

Conventional methods of detecting Fusarium spp. infection, which causes significant economic losses in potato production, are time-consuming and expensive. This study explored rapid and non-destructive detection techniques using visible/near-infrared (Vis/NIR) spectroscopy. Potato seeds of the Granola L variety were intentionally infected with Fusarium spp. by fungal inoculation, then stored at 12°C, 25°C, and a combination of both. Healthy potatoes were stored under the same conditions in containers for 30 days and monitored every five days. Principal component analysis-linear discriminate analysis (PCA-LDA) was used to classify potato tubers based on their infection status. PCA-LDA analysis revealed significant spectral differences between healthy and infected potato seeds across all storage temperatures. Calibration reliability values were 95.87% (for samples stored at 12°C), 97.52% (stored at 25°C), and 98.35% (for the combination of 12°C and 25°C). Similar trends were observed for accuracy: 91.96% (12°C), 98.29% (25°C), and the highest accuracy of 98.65% for the combined temperature. These techniques facilitate rapid identification of infections, aiding farmers and producers in implementing more efficient preventive actions, resulting in decreased crop losses and waste products and enhanced productivity in the agricultural sector.

Raman spectroscopy combined with multivariate statistical algorithms for the simultaneous screening of cervical and breast cancers.

Breast and cervical cancers are becoming the leading causes of death among women worldwide, but current diagnostic methods have many drawbacks, such as being time-consuming and high cost. Raman spectroscopy, as a rapid, reliable, and non-destructive spectroscopic detection technique, has achieved many breakthrough results in the screening and prognosis of various cancer tumors. Therefore, in this study, Raman spectroscopy technology was used to diagnose breast cancer and cervical cancer. A total of 225 spectra were recorded from 87 patients with cervical cancer, 60 patients with breast cancer, and 78 healthy individuals. The obvious difference in Raman spectrum between the three groups was mainly shown at 809cm-1 (tyrosine), 958cm-1 (carotenoid), 1004cm-1 (phenylalanine), 1154cm-1 (β-carotene), 1267cm-1 (Amide III), 1445cm-1 (phospholipids), 1515cm-1 (β-carotene), and 1585cm-1 (C = C olefinic stretch). We used one-way analysis of variance for these peaks and demonstrated that they were significantly different. Then, we combined the detected Raman spectra with multivariate statistical calculations using the principal component analysis-linear discrimination algorithm (PCA-LDA) to discriminate between the three groups of collected serum samples. The diagnostic results showed that the model's accuracy, precision, recall, and F1 score of the model were 92.90%, 92.62%, 92.10%, and 92.36%, respectively. These results suggest that Raman spectroscopy can achieve ultra-sensitive detection of serum, and the developed diagnostic models have great potential for the prognosis and simultaneous screening of cervical and breast cancers.

Non-destructive techniques for corrosion detection: A review

Corrosion detection (CD) has become a high priority in chemical industries, defense and transportation sectors to extend the life of existing or new systems while ensuring the safety of the existing components and reducing downtime to minimise economic losses. Here, this article is aimed to review and discuss non-destructive techniques used widely in the industry to detect, monitor and repair corrosion problems early on. The comprehensive review provides a detailed discussion, a functional mechanism, advantages and disadvantages of crucial non-destructive CD techniques widely used in the industry, helping the reader choose the type of corrosion monitoring methods effectively. An extensive literature review of visual and optical testing techniques, acoustic emissions, eddy current, guided wave and equipment, infrared thermography, radiographic, microwave and millimeter wave, and terahertz imaging is discussed. The underlying mechanism, its merits and limitations, along with the usage scenario, is explained that can be related across different areas making it interdisciplinary research for corrosion monitoring techniques.

The effect of manufacture process on material properties of laminated composite cylinders using non-destructive technique

This paper presents a comparative investigation of the effect of hand lay-up to manufacture of composite structure on the elastic constants of composite cylindrical shells. Non-destructive technique for detection of elastic constants in laminated composite cylinders were applied. This technique based on numerical-experimental method that included experimental testing and numerical modelling. Experimental testing includes determination of natural frequencies in composite cylinders fixed at the ends. Composite construction fixed by upper end on the special test bench to realize clamped-free boundary conditions. 48 piezo sensors were glued on the outer surface of composite shells to measure strains during testing. Data received during experiments were converted in modal parameters using ARTEMIS software. By minimizing the error functional between the numerical and experimental data of the natural frequencies, the properties of laminated composite materials were obtained. It can be seen that elastic constants of the five composite shells have insignificant differences in values and be successfully applied for numerical calculation in further.

A Systematic Review and Comparative Meta-analysis of Non-destructive Fruit Maturity Detection Techniques

The global fruit industry is growing rapidly due to increased awareness of the health benefits associated with fruit consumption. Fruit maturity detection plays a crucial role in fruit logistics and maintenance, enabling farmers and fruit industries to grade fruits and develop sustainable policies for enhanced profitability and service quality. Non-destructive fruit maturity detection methods have gained significant attention, especially with advancements in machine vision and spectroscopic techniques. This systematic review provides a concise overview of the techniques and algorithms used in fruit quality grading by farmers and industries. The study reviewed 63 full-text articles published between 2012 and 2023 along with their bibliometric analysis. Qualitative analysis revealed that researchers from various disciplines contributed to this field, with techniques falling into 3 categories: machine vision (mathematical modelling or deep learning), spectroscopy and other miscellaneous approaches. There was a high level of diversity among these categories, as indicated by an I-square value of 88.37% in the heterogeneity analysis. Meta-analysis, using odds ratios as the effect measure, established the relationship between techniques and their accuracy. Machine vision showed a positive correlation with accuracy across different categories. Additionally, Egger's and Begg's tests were used to assess publication bias and no strong evidence of its occurrence was found. This study offers valuable insights into the advantages and limitations of various fruit maturity detection techniques. For employing statistical and meta-analytical methods, key factors such as accuracy and sample size have been considered. These findings will aid in the development of effective strategies for fruit quality assessment.

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.

Quantitative analysis of cephalexin in solid dosage form by Raman spectroscopy and chemometric tools

Objective To use Raman Spectroscopy for qualitative and quantitative evaluation of pharmaceutical formulations of active pharmaceutical ingredient (API) of Cephalexin. Significance Raman Spectroscopy is a noninvasive, nondestructive, reliable and rapid detection technique used for various pharmaceutical drugs quantification. The present study explores the potential of Raman Spectroscopy for quantitative analysis of pharmaceutical drugs. Method For qualitative and quantitative analysis of Cephalexin API, various standard samples containing less and more concentration of API than commercial tablet was prepared. To study spectral differences, the mean plot of all the samples was prepared. For qualitative analysis, Principal Component Analysis (PCA) and for quantitative analysis Partial Least Square Regression analysis (PLSR) was used. Both of these are Multivariate data analysis techniques and give reliable results as published in previous literature. Results PCA model distinguished all the Raman Spectral data related to the various Cephalexin solid dosage formulations whereas the PLSR model was used to calculate the concentration of different unknown formulations. For the PLSR model, RMSEC and RMSEP were determined to be 3.3953 and 3.8972, respectively. The prediction efficiency of this built PLSR model was found to be very good with a goodness of the model value (R2) of 0.98. The PLSR model also predicted the concentrations of Cephalexin formulations in the blind or unknown sample. Conclusion These findings demonstrate that the Raman spectroscopy coupled to PLSR analysis could be regarded as a fast and effectively reliable tool for quantitative analysis of pharmaceutical drugs.

A boundary integration method for calculating diffraction intensity of crystalline materials with planar deformation

Defects and strain states in crystals can be investigated using X-ray and neutron diffraction, two types of penetrating, non-destructive detection techniques. Various approximations are frequently employed to minimize the amount of computation in the practical calculations because the present Fourier transform approach, established by Warren, Averbach et al. based on the Kinematic diffraction theory, has a huge computational cost. This paper develops a numerical method to precisely calculate diffraction intensity by integrating kinematic diffraction amplitude over the boundary, which is applicable to crystals with planar deformation. The precision of this method is assessed by contrasting it with Wilson's analytical equations for the X-ray diffraction results of a single screw dislocation. As an example, the difference in diffraction spots on a single edge dislocation irradiated by different incident X-ray widths are calculated and analyzed. When compared to the X-ray diffraction spots of an ideal material with a dislocation core dug out, the results indicate that if lattice information close to the dislocation core is to be retrieved by X-ray diffraction, the X-ray irradiation area should be restricted.

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.

Extraction and imaging of indicator elements for non-destructive, in-situ, fast identification of adverse geology in tunnels

The lag in quantitative methods and detection techniques for geologic information has resulted in time-consuming and human-experienced geologic analysis in tunnels. Geochemical indicators of rocks can be used to identify adverse geology and to explain the intrinsic causes of damage to normal rocks. This study proposes a method to identify adverse geology by extracting and imaging the indicator elements. The mapping relationship between rock components and geologic bodies is quickly determined by indicator element extraction based on factor analysis, and then the data are gridded for image output. The location and size of the target adverse geology are visually identified through the distribution images of the indicator elements, thus reducing data dimensions and analysis time. A non-destructive, in-situ and fast element detection technique in tunnels was adopted to speed up the process of geology identification. The accuracy of the detection was validated by comparing field and laboratory test results. This study further confirms and refines the previous research, and the results provide references for geological, mining and underground projects.

Terahertz label-free detection of nicotine-induced neural cell changes and the underlying mechanisms

Nicotine exposure can lead to neurological impairments and brain tumors, and a label-free and nondestructive detection technique is urgently required by the scientific community to assess the effects of nicotine on neural cells. Herein, a terahertz (THz) time-domain attenuated total reflection (TD-ATR) spectroscopy approach is reported, by which the effects of nicotine on normal and cancerous neural cells, i.e., HEB and U87 cells, are successfully investigated in a label/stain-free and nondestructive manner. The obtained THz absorption coefficients of HEB cells exposed to low-dose nicotine and high-dose nicotine are smaller and larger, respectively, than the untreated cells. In contrast, the THz absorption coefficients of U87 cells treated by nicotine are always smaller than the untreated cells. The THz absorption coefficients can be well related to the proliferation properties (cell number and compositional changes) and morphological changes of neural cells, by which different types of neural cells are differentiated and the viabilities of neural cells treated by nicotine are reliably assessed. Collectively, this work sheds new insights on the effects of nicotine on neural cells, and provides a useful tool (THz TD-ATR spectroscopy) for the study of chemical-cell interactions.

Facile electrochemical surface-alloying and etching of Au wires to enable high-performance substrates for surface enhanced Raman scattering

Surface-enhanced Raman Spectroscopy (SERS) is a nondestructive technique for rapid detection of analytes even at the single-molecule level. However, highly sensitive and reliable SERS substrates are mostly fabricated with complex nanofabrication techniques, greatly restricting their practical applications. A convenient electrochemical method for transforming the surface of commercial gold wires/foils into silver-alloyed nanostructures is demonstrated in this report. Au substrates are treated with repetitive anodic and cathodic bias in an electrolyte of thiourea, in a one-pot one-step manner. X-rays absorption fine structure (XAFS) spectroscopy confirms that the AuAg alloy is induced at the surface. The unique AuAg alloyed surface nanostructures are particularly advantageous when served as SERS substrates, enabling a remarkably sensitive detection of Rhodamine B (a detection limit of 10−14 ​M, and uniform strong response throughout the substrates at 10−12 ​M).

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.

Detection of changes in dynamic characteristics of composite structures using Kriging metamodeling procedure: Experimental and computational analysis

To ensure the quality, safety, and long-term durability of fiber-reinforced composite components, it is necessary to explore approaches for monitoring and detecting defects or damage. Nondestructive detection techniques based on dynamic properties and structural response, such as natural frequency, damping, and modal shape. This study presents a numerical-experimental methodology based on dynamic analysis of structures made of composite materials. The methodology utilizes a surrogate model to establish a design envelope through a Kriging metamodel and to assess a damage index for structures affected by impact damage. The Latin Hypercube method is employed to generate values for the input variables, while the finite element method is utilized to calculate the natural frequencies. The Kriging metamodel is then employed to generate a numerical model, which is optimized using the Efficient Global Optimization algorithm and the expected improvement metric to minimize computational costs. The methodology yields a frequency range and determines a design envelope to evaluate the manufacturing quality of the structure. A damage index is used to identify structures with defects or impact damage, allowing for the assessment of severity. Additionally, the study evaluates the impact of incorporating metrics into the Latin Hypercube method to further reduce computational costs. Finally, this proposed approach contributes to the development of monitoring systems for assessing the manufacturing quality of composite structures and detecting impact damage through dynamic analysis. By utilizing this methodology, it becomes possible to effectively identify and evaluate the severity of defects and damage in composite structures, thus enhancing quality evaluation processes.

Nondestructive Detecting Maturity of Pineapples Based on Visible and Near-Infrared Transmittance Spectroscopy Coupled with Machine Learning Methodologies

Pineapple is mainly grown in tropical regions and consumed fresh worldwide due to its attractive flavor and health benefits. With increasing global production and trade volume, there is an urgent need for nondestructive techniques for accurate and efficient detection of the internal quality of pineapples. Therefore, this study is dedicated to developing a nondestructive method for real-time determining the internal quality of pineapples by using VIS/NIR transmittance spectroscopy technique and machine learning methodologies. The VIS/NIR transmittance spectrums ranging in 400–1100 nm of total 195 pineapples were collected from a dynamic experimental platform. The maturity grade and soluble solids content (SSC) of individual pineapples were then measured as indicators of internal quality. The qualitative model for discriminating maturity grades of pineapple achieved a high accuracy of 90.8% by the PLSDA model for unknown samples. Meanwhile, the quantitative model for determining SSC also reached a determination coefficient (RP2) of 0.7596 and a root mean square error of prediction (RMSEP) of 0.7879 °Brix by the ANN-PLS model. Overall, high model performance demonstrated that using VIS/NIR transmittance spectroscopy technique coupled with machine learning methodologies could be a feasible method for nondestructive and real-time detection of the internal quality of pineapples.