Search

This article presents the application of infrared thermography as a nondestructive testing method (NDT) for detecting osmotic damage in glass-fiber-reinforced polymer (GFRP) and glass-reinforced polymer (GRP) boat hull structures. The aim of the conducted experiments is to explore the possibilities of applying active infrared thermography to real structures and to establish a procedure capable of filtering out anomalies caused by various thermal influences, such as thermal reflections from surrounding objects, geometry effects, and heat flow variations on the observed object. The methods used for post-processing IR signals include lock-in thermography (LT), pulse thermography (PT), pulse phase thermography (PPT), and gradient pulse phase thermography (GT). The practical application and advantages and disadvantages of infrared thermography in identifying osmotic damage in GFRP and GRP boat hulls will be illustrated through three case studies. Each case study is based on specific conditions and characteristics of different types of osmotic damage, enabling a thorough analysis of the effectiveness of the method in detecting and assessing the severity of the damage. The post-processed thermal images enable a clearer distinction between damaged and undamaged zones, improving the robustness of detection under realistic field conditions. [ABSTRACT FROM AUTHOR]

Background: Accurate reconstruction of internal temperature fields from surface temperature data is critical for applications such as non-invasive thermal imaging, particularly in scenarios involving small temperature gradients, like those in the human body. Methods: In this study, we employed 3D convolutional neural networks (CNNs) to predict internal temperature fields. The network's performance was evaluated under both ideal and non-ideal conditions, incorporating noise and background temperature variations. A physics-informed loss function embedding the heat equation was used in conjunction with statistical uncertainty during training to simulate realistic scenarios. Results: The CNN achieved high accuracy for small phantoms (e.g., 10 cm in diameter). However, under non-ideal conditions, the network's predictive capacity diminished in larger domains, particularly in regions distant from the surface. The introduction of physical constraints in the training processes improved the model's robustness in noisy environments, enabling accurate reconstruction of hot-spots in deeper regions where traditional CNNs struggled. Conclusions: Combining deep learning with physical constraints provides a robust framework for non-invasive thermal imaging and other applications requiring high-precision temperature field reconstruction, particularly under non-ideal conditions. [ABSTRACT FROM AUTHOR]

[Objective] To achieve accurate and non-destructive testing of key quality indicators of fresh grapes, we proposed a dual-channel improved convolutional neural network (CNN) method for fresh grape quality inspection. [Methods] A fiber-optic spectrometer and a CCD camera were used to collect visible near-infrared spectra and characterize the image information of grape samples. The dual channel improved CNN model was established and the Gramian angular field (GAF) was used to convert one-dimensional spectral data into two-dimensional images for the extraction of effective features from spectra data. The convolutional layers with different sizes of convolution kernels were designed to extract features from the converted spectral two-dimensional images and grape characterization images, thus enhancing the comprehensive perception ability of the CNN model. On this basis, the dropout method was used for the fully connected layer to reduce and fuse the extracted features, ultimately achieving accurate prediction of key indicators of grape quality. [Results] Compared with other three grape quality inspecting methods, the CNN method showed the root mean square error decreases of 50.48%, 57.44%, and 49.56% and the correlation coefficient increases of 4.89%, 3.13%, and 2.17%, respectively. [Conclusion] The dual-channel improved CNN method can achieve non-destructive testing of key indicators of grape quality [ABSTRACT FROM AUTHOR]

In recent decades, composites have been widely employed in various sectors to reduce the weight of structural materials whilst enhancing their mechanical qualities. This issue of Textile Progress provides a detailed overview of composite material types, failure mechanisms, and damage-detection techniques applicable to composite structures. Due to their heterogeneity, composite materials are susceptible to several complicated forms of damage. Thus, for early damage identification of composites, structural health monitoring (SHM) may assist in efficient maintenance-scheduling, reducing wasteful inspections, and avoiding catastrophic occurrences and tragic outcomes. SHM is more sophisticated than most non-destructive testing approaches in that it involves establishing systems capable of continually gathering, analyzing, and interpreting data from structures to evaluate their health and lifespan. Several SHM approaches for composite structural-damage diagnosis are available; however, none are adequate for all situations. The number of techniques accessible now is too large to discuss in a single study so methods that may be used as a foundation for further research are the main focus of the current review. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of International Journal of Engineering Transactions C: Aspects is the property of International Journal of Engineering (IJE) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Non-destructive testing methods, such as Ultrasonic Pulse Velocity (UPV), have been widely used to assess the quality and performance of cementitious stabilised aggregates. The present study reveals the robust relationship of UPV and constrained modulus (CM) with unconfined compressive strength (UCS), modulus of rupture/flexural strength (MoR), and flexural modulus (FM) in aggregates stabilised with five different stabiliser combinations, including cement, cement-ground granulated blast furnace steel slag (GGBS), cement-fly ash and cement with two commercial chemical admixtures. The high accuracy of UPV and CM in estimating destructive test parameters and approximating challenging tests like FM is convincingly demonstrated with strong correlations (R² > 0.80) and standard error ratios (Se/Sy < 0.35). Power law and logarithmic curves prove suitable for modelling the relationships between strength and stiffness with UPV and CM, respectively. Notably, however, the study reveals significant variations in correlating parameters across different cementitious stabilisers within the same mix design, emphasising the strong dependence on the specific cementitious material type. Overall, this work offers valuable insights into the use of non-destructive parameters, UPV and CM, for the characterisation of stabilised aggregates based on cementitious material type and different curing conditions. [ABSTRACT FROM AUTHOR]

In the category of new and advanced materials, carbon fiber reinforced plastic (CFRP) composite materials are used in areas such as structural materials in aeronautics, transport, etc. The bi-phasic structure of CFRP requires knowledge of both fiber and matrix properties. In the conditions where possible delamination's occur during use, these depend both on the properties of the interfaces and of the interlaminate. The appropriate ultrasound (US) techniques allow the determination of the propagation speed of the longitudinal and transverse waves which are used in the evaluation of the elastic modulus E, shear modulus G on the three principal directions. C-scan US using phased array allows the emphasizing and characterization of ones with porosities that appear during composite fabrication or due to local overheating. The results are compared with those obtained by a dynamic mechanical analyzer (DMA), being found a good correlation. These procedures allow also the emphasizing of matrix damages due to high temperature used or establishing maximum temperature for used. [ABSTRACT FROM AUTHOR]

Impulse Response testing is a widespread geophysical technique of monolithic plate-like structures (foundation slabs, tunnel lining, and supports for vertical, inclined and horizontal mine shafts, retaining walls) contact state and grouting quality evaluation. Novel approach to data processing based on normalized response attributes analysis is presented. It is proposed to use the energy of the normalized signal calculated in the time domain and the normalized spectrum area and the average-weighted frequency calculated in the frequency domain as informative parameters of the signal. The proposed technique allows users a rapid and robust evaluation of underground structure’s grouting or contact state quality. The advantage of this approach is the possibility of using geophysical equipment designed for low strain impact testing of piles length and integrity to collect data. Experimental study has been carried out on the application of the technique in examining a tunnel lining physical model with a known position of the loose contact area. As examples of the application of the methodology, the results of the several monolitic structures of operating municipal and transport infrastructure underground structures survey are presented. The applicability of the technique for examining the grouting of the tunnel lining and the control of injection under the foundation slabs is confirmed. For data interpretation the modified three-sigma criteria and the joint analysis of the attribute’s behavior were successfully used. The features of the field work methodology, data collection and analysis are discussed in detail. Approaches to the techniques' development and its application in the framework of underground constructions monitoring are outlined. The issues arising during acoustic examination of reinforced concrete plate-like structures are outlined.

A method utilizing vibroacoustic nondestructive testing technology was proposed to address the difficulty of detecting early internal browning in pears. It combined the S-transform signal analysis method and MobileNetV3 neural network to distinguish pears with different degrees of browning. After converting the one-dimensional response signal into a two-dimensional time–frequency image through the S transform, these time–frequency images were then categorized into healthy, slight browning, and severe browning pears based on the actual degree of browning observed in the pears. These three classes of datasets were inputted into an improved MobileNetV3 model, which incorporates a random cropping module (RC) and attention mechanism (CA). The constructed MobileNetV3-RA model achieved an overall classification accuracy of 96.79%. Specifically, the accuracy values were as follows: 97.50% for healthy pears, 93.75% for slightly browning pears, and 95.83% for severely browning pears. Notably, on the imbalanced test set, the F1, Ka, MCC, and IAM values for the MobileNetV3-RA model were 94.05%, 91.72%, 91.57%, and 83.97%, respectively. Additionally, MobileNetV3-RA has faster detection speed and fewer parameters, with respective values of 0.27s and 4.45M, compared to other common classification models. Therefore, this study provides an effective nondestructive testing method for early internal browning of pears and provides some research ideas and methods for internal quality detection of other fruits and vegetables. [ABSTRACT FROM AUTHOR]

Laminated veneer lumber panels (LVL) are engineered wood products suitable for application in construction contexts. However, LVL panels have some deficient elastic properties (e.g., E 22 ) concerning other elastic properties (e.g., E 11 and G 12 ), which may cause problems in structural applications. Carbon and basalt fibers (CF and BF) are reinforcement alternatives for LVL panels, as they can be included in the interior or exterior wood veneer bonding process. This work aims to analyze the effect of incorporating CF and BF fibers in the orthotropic elastic properties of radiata pine LVL panels through a nondestructive method based on transverse vibration tests and model updating techniques. Accordingly, 20 LVL panels of 15 mm thickness were fabricated and tested with different reinforcing fibers and adhesives. Then, some relevant panels' dynamic properties were identified through experimental modal analysis. Finally, three relevant panels' orthotropic elastic properties were estimated simultaneously using finite-element model updating techniques and Python-based deterministic calibration scripts. The results suggest that the reinforced LVL panels obtained significant increases in their orthotropic elastic properties, in the order of 22%, 333%, and 27% for E 11 , E 22 , and G 12 , respectively. These results show the effectiveness of the type of reinforcement applied and the potential application of the nondestructive evaluation method in other contexts. [ABSTRACT FROM AUTHOR]

This paper reviews structural health monitoring (SHM) techniques of bridge structures based on machine learning (ML) algorithms. Regular inspections and the use of non-destructive testing are still the common damage-detection methods; however, they are susceptible to subjectivity and human error and involve prolonged duration. With emerging technologies such as artificial intelligence and the development of wireless sensors, SHM has shifted from offline model-driven damage detection to online/real-time data-driven damage detection. In this paper, both supervised and unsupervised ML algorithms are examined to determine which of the latest methods would be the most suitable and effective for the SHM of bridge structures. This review paper investigates recent studies on data acquisition, data imputation, data compression, feature extraction and pattern recognition using supervised/unsupervised ML algorithms. [ABSTRACT FROM AUTHOR]

Adhesive joints are extensive used in various industrial applications. Bonded quality is crucial for ensuring structural integrity and safety. In this study, the nonlinear non-collinear wave mixing techniques were developed to nondestructive evaluate micro imperfections in adhesive joints, including adhesive degradation and bond weakening. Two testing schemes were proposed via the resonance conditions of the adhesive and substrate respectively, following the classical nonlinearity theories. Numerical simulations and experiments of non-collinear wave mixing were conducted to explore the feasibility of the proposed testing schemes for accessing two typical micro imperfections in adhesive joints. Both the simulation and experimental results demonstrate that the proposed nonlinear non-collinear wave mixing method is effective for nondestructive evaluation of the micro imperfections in adhesive joints. Moreover, the scheme via resonance conditions of adhesive exhibits a higher sensitive to the adhesive degradation, whereas the one relying on the resonance conditions of substrate exhibits a higher sensitive to bond weakening. [ABSTRACT FROM AUTHOR]

Instance segmentation of compound objects in XXL-CT imagery poses a unique challenge in non-destructive testing. This complexity arises from the lack of known reference segmentation labels, limited applicable segmentation tools, as well as partially degraded image quality. To asses recent advancements in the field of machine learning-based image segmentation, the 'Instance Segmentation XXL-CT Challenge of a Historic Airplane' was conducted. The challenge aimed to explore automatic or interactive instance segmentation methods for an efficient delineation of the different aircraft components, such as screws, rivets, metal sheets or pressure tubes. We report the organization and outcome of this challenge and describe the capabilities and limitations of the submitted segmentation methods. [ABSTRACT FROM AUTHOR]

This work follows on from studies carried out by Jiles and Atherton on magnetic hysteresis and on the magnetomechanical effect. The Jiles–Atherton equation, which models the rate of change of magnetization with respect to stress, was solved numerically in the forward and reverse directions for stress up to 90 MPa, suggesting that stressing carbon steel will cause a lasting change in the magnetization of the sample. This was confirmed experimentally by measuring the B-field in proximity to samples of C45 steel while undergoing tensile stress, with the pattern of magnetization suggesting that the magnetic domains reorient themselves in the geomagnetic field when stressing loosens their pinning. A further experiment on two samples confirms this, with the B-field around the samples showing strong changes according to their orientation in the geomagnetic field at the time of the stressing. This work has relevance to the non-destructive testing of steel structures such as pipelines, and the relevance of the experiments to this work is considered, as well as future prospects. [ABSTRACT FROM AUTHOR]

With increasing demand and environmental concerns, researchers are exploring new materials that can perform as well or better than traditional materials while reducing environmental impact. The BAMline, a real-life sample materials research beamline, provides unique insights into materials' electronic and chemical structure at different time and length scales. The beamline specializes in x-ray absorption spectroscopy, x-ray fluorescence spectroscopy, and tomography experiments. This enables real-time optimization of material properties and performance for various applications, such as energy transfer, energy storage, catalysis, and corrosion resistance. This paper gives an overview of the analytical methods and sample environments of the BAMline, which cover non-destructive testing experiments in materials science, chemistry, biology, medicine, and cultural heritage. We also present our own synthesis methods, processes, and equipment developed specifically for the BAMline, and we give examples of synthesized materials and their potential applications. Finally, this article discusses the future perspectives of the BAMline and its potential for further advances in sustainable materials research. [ABSTRACT FROM AUTHOR]

Composite materials find increasing applications in modern industry and transport. However, they may lose desirable mechanical properties due to external mechanical excitations, ultraviolet radiation, moisture penetration, or other factors. Therefore, an effective way to assess the condition of materials is necessary. In this article, a nondestructive evaluation of glass fiber-reinforced composite subjected to five-stage static bending is presented. For this reason, pulsed excitation terahertz imaging was utilized, and a data processing/exploration scheme was proposed. The proposed, novel approach consists of an efficient data registration algorithm based on surface approximation (for surface roughness and unevenness elimination) and a parametrization scheme applied for the signals gained from the time response of the glass fiber-reinforced polymer layer. Obtained parameters enable the global description of the evaluated material state and prediction of failure effectively, even in the early stages of destruction.

Abstract Background Wheat (Triticum aestivum L.) is an important grain crops in the world, and its growth and development in different stages is seriously affected by saline-alkali stress, especially in seedling stage. Therefore, nondestructive detection of wheat seedlings under saline-alkali stress can provide more comprehensive technical support for wheat breeding, cultivation and management. Results This research focused on moisture signal prediction and classification of saline-alkali stress in wheat seedlings using fusion techniques. After collecting and analyzing transverse relaxation time and Multispectral imaging (MSI) information of wheat seedlings, four regression models were used to predict the moisture signal. K-Nearest Neighbor (KNN) and Gaussian-Naïve Bayes (GNB) models were combined with fivefold cross validation to classify the prediction of wheat seedling stress. The results showed that wheat seedlings would increase the bound water content through a certain mechanism to enhance their saline-alkali stress. Under the same Na concentration, the effect of alkali stress on moisture, growth and spectrum of wheat seedlings is stronger than salt stress. The Gradient Boosting Decision Regression Tree model performs the best in predicting wheat moisture signals, with a coefficient of determination (R2P) of 0.98 and a root mean square error of 109.60. It also had a short training time (1.48 s) and an efficient prediction speed (1300 obs/s). The KNN and GNB demonstrated significantly enhanced predictive performance when classifying the fused dataset, compared to using single datasets individually. In particular, the GNB model performing best on the fused dataset, with Precision, Recall, Accuracy, and F1-score of 90.30, 88.89%, 88.90%, and 0.90, respectively. Conclusions Under the same Na concentration, the effects of alkali stress on water content, spectrum, and growth of wheat were stronger than that of salt stress, which was more unfavorable to the growth of wheat. The fusion of low-field nuclear magnetic resonance and MSI technology can improve the classification of wheat stress, and provide an effective technical method for rapid and accurate monitoring of wheat seedlings under saline-alkali stress. Graphical Abstract

To enrich the technical means of quality control of Torreya, the qualitative analysis model of storage time and conditions of Torreya grandis was established by near infrared spectroscopy. Additionally, a quantitative analysis model for protein and fat content of Torreya was established using chemometric methods. After preprocessing the spectra and selecting feature wavelengths, a convolutional neural network (CNN) model based on genetic algorithm (GA) and multiplicative scatter correction (MSC) algorithm was developed, achieving an identification accuracy of 99.2% in distinguishing Torreya with different storage times and conditions. Furthermore, partial least squares (PLS), radial basis function neural network (RBF), and extreme learning machine (ELM) models were established and compared. Competitive adaptive reweighted sampling (CARS) was used as a spectral feature selection method, resulting in 41 and 56 selected features for protein and fat content, respectively, significantly improving computational efficiency. The results showed that the D2-CARS-PLS model with second derivative (D2) preprocessing and CARS method was the optimal model, achieving good prediction performance for protein and fat content of Torreya with coefficients of determination (R2) of 0.977 and 0.984, respectively. The findings demonstrated the potential of near-infrared spectroscopy technology for rapid and non-destructive detection of Torreya nut quality, providing a reliable analytical method for Torreya nut quality assessment.

Highlights Three‐dimensional layer‐to‐layer interlock woven composites (3D LTLIWCs) have been widely used in various aero‐engine blade models because of their excellent near‐net forming capabilities for complex components, as well as significant advantages in structural design flexibility and high damage tolerance. In this paper, a typical and two new 3D LTLIWCs, specifically including 1/3 twill (S‐I), modified satin (S‐II), and stuffer twill (S‐III), were manufactured by two design methods: varying interweaving frequency and introducing stuffer yarns. Three‐point bending and uniaxial tensile tests were conducted along the 0° (warp) and 90° (weft) directions. The damage morphology and failure mechanism of the specimens were revealed by nondestructive testing technology and topological structure analysis. The results showed that the fabric structure significantly influenced the mechanical properties and failure mechanisms of the 3D LTLIWCs. Compared with S‐I and S‐II, S‐III demonstrated a 17.9%–80.2% increase in bending strength and a 35.3%–162.8% increase in tensile strength in the 0° direction, while the bending strength and tensile strength in the 90° direction increased by 1.1%–73.3% and 9.3%–50.4%, respectively. Notably, S‐III exhibited lower bending and tensile damage in the 0° direction than S‐I and S‐II, with a smaller propagation range of resin and interface cracks and less severe shear fracture of the load‐bearing yarns. This study can provide a useful reference for the optimal design of fabric structure for 3D woven composite blades. Two design schemes for optimizing 3D LTLI fabric structure were compared. Introducing stuffer yarns improved the mechanical properties of 3D LTLIWCs. S‐III composites exhibited the highest failure strength in all test directions. Full‐field strain distribution was closely related to the interlocking patterns. Fabric structures had a great influence on the failure modes of 3D LTLIWCs. [ABSTRACT FROM AUTHOR]

\nHighlightsThis study aims to enhance the detection and characterisation of anomalies in manufactured parts by integrating machine learning (ML) with resonance frequency spectra data. A key contribution of this work is the development of a novel Impulse Excitation Technique (IET)-based method that effectively evaluates material health and identifies subtle defects by leveraging numerous mathematical and physical metrics as input features. Three machine learning models – Random Forest (RF), K-Nearest Neighbor (KNN), and Multi-layer Perceptron (MLP) – were systematically compared to determine the most effective method for classifying defects, specifically focusing on healthy, cracked, and dimensionally deviated samples. Among these, the MLP model demonstrated the highest performance, achieving Receiver Operating Characteristic (ROC) values of 0.963, 0.901, and 0.942 for each class, respectively. Additionally, SHAP (SHapley Additive exPlanations) analysis showed anomalies were sensitive to specific resonance frequency metrics, improving prediction accuracy. Cracked samples exhibited slight peak broadening and negative peak shifts, while dimensionally deviated samples showed positive and negative shifts and missing peaks. Dimensional deviations were more pronounced than cracks, making them easier to identify and enhancing predictive accuracy.A novel ML framework was developed for anomaly detection in manufacturing.Among the ML algorithms, the MLP achieved the highest accuracy.Each class of anomalies is sensitive to specific resonance frequency metrics.Dimensionally deviated samples are predicted more accurately than cracked samples.Different types of defects have specific effects on the frequency spectrum.A novel ML framework was developed for anomaly detection in manufacturing.Among the ML algorithms, the MLP achieved the highest accuracy.Each class of anomalies is sensitive to specific resonance frequency metrics.Dimensionally deviated samples are predicted more accurately than cracked samples.Different types of defects have specific effects on the frequency spectrum. [ABSTRACT FROM AUTHOR]

The swift progress in portable spectrometer technology has fostered a widespread need for compact light sources. Concurrently, near-infrared phosphor-converted light-emitting diodes (NIR pc-LEDs), characterized by their low cost and compact structure, emerge as an optimal choice. Inspired by phase optimization through fluxes, we successfully synthesized pure phase Lu 3 Mg 2 GaSi 2 O 12 (LMGS):Cr3+, 3 wt%MgF 2 luminescent materials by incorporating various fluxes including LiF, NaF, MgF 2 , CaF 2 , SrF 2 and BaF 2. These materials emit broadband NIR light ranging from 620 nm to 1150 nm, with full width at half-maximums (FWHMs) exceeding 125 nm. By employing a dual-fluxes strategy to further enhance the luminescence intensity of Cr3+, the LMGS:3%Cr3+, 1 wt%NaF, 2 wt%MgF 2 sample exhibits a 2.45 times increase in emission intensity compared to the LMGS:3%Cr3+, 3 wt%MgF 2 sample. Concurrently, the quantum efficiency rises from 24 % to 46 %. Moreover, the integrated emission intensity of LMGS:3%Cr3+, 1 wt%NaF, 2 wt%MgF 2 retains at 75 %@425 K of its value at RT. Upon coupled with a 430 nm LED chip, the luminescent material exhibits an NIR output power of 43.8 mW@300 mA, indicating promising applications in rapid night identification, night vision and non-destructive testing. By adding different fluxes, the luminescence intensity of LMGS:Cr3+ is achieved a stepwise enhancement. [Display omitted] [ABSTRACT FROM AUTHOR]

We developed a method to evaluate the failure rate (FR) for the cylindrical part of a composite high-pressure hydrogen vessel with arbitrary dimensions, number of layers, and stacking sequences by giving the mean and standard deviation of the fiber strength development rate and the stiffness reduction rate under various damage types. The FR is the rate at which the residual strength of a vessel is below the maximum service pressure (125%NWP). Assuming a case in which the vessel is designed with the damage-tolerance design, the FR was calculated for the case of all-layer matrix cracking (assuming beginning-of-life) and fiber failure in two layers in addition to the matrix cracking (assuming the most severe damage that is tolerable during service). The observations demonstrate the importance of improving the probability of detection for non-destructive testing in ensuring the safety of damage-tolerance-designed vessels after fiber failure. The mean strength of the fibers mainly affected the mean residual strength of the composite high-pressure hydrogen vessels when the dimensions and stacking sequence remained unchanged. [ABSTRACT FROM AUTHOR]

This study explores the effect of strain on Magneto Acoustic Emission (MAE) characteristics in Q235 steel plates under various excitation frequencies and voltages. Using Maximal Overlap Discrete Wavelet Transform (MODWT), we processed envelope energy signals derived from MAE signals generated from samples subjected to incremental tensile forces revealing critical variations in features extracted from AC and DC components such as RMS, mean bias and energy distribution. The results demonstrate the significant impact of strain on MAE signal characteristics, particularly under higher excitation frequencies and voltages, which enhances the detection of subtle plastic deformations. Our approach provides a refined methodology for enhancing MAE-based Non-Destructive Testing (NDT) practices, crucial for the reliable assessment of material integrity in engineering applications. [ABSTRACT FROM AUTHOR]

Laser ultrasonic testing is an advanced non-destructive testing with high frequency and fast detection speed. However, most laser ultrasonic testing characterises defects by manually extracting the features of signals. To address this, deep learning is introduced to identify defects intelligently. Firstly, theoretical models based on finite elements are built to study the the surface and subsurface defects’ effects on surface acoustic wave (SAW) propagation. Experimental studies are also conducted on 5 samples with surface defects and 15 with subsurface defects. Then, Gaussian white noise is added to both simulation and experimental signals to expand the data set and imitate the complex conditions. The wavelet transform images of the reflected and transmitted signal are combined into a data set for training the convolutional neural network (CNN). The subsurface defects’ data set is similarly augmented. Finally, a total of 40,000 surface defect and 21,000 subsurface defect data are collected and split into training, test, and validation sets (6:2:2 ratio). The CNN accurately classifies surface defect penetration depth (minimum error 5.87%), while subsurface defect widths are determined with transfer learning (minimum error 5.6%). It proves that it is feasible to classify and identify defects by combining the laser ultrasonic method with deep learning. [ABSTRACT FROM AUTHOR]

Rolling bearing fault diagnosis enhances equipment reliability, reduces maintenance costs, and enables effective non-destructive testing (NDT). However, current research often emphasizes model design and performance optimization, overlooking the long-term dependencies of fault signals and the need for interpretability. This study proposes a rolling bearing fault diagnosis model utilizing a time-series fusion transformer with interpretability analysis. The model introduces multi-scale feature adaptive fusion to automatically capture and integrate features across different scales, enhancing the global pattern detection in time-series data. A dynamic patch auto-encoder module transforms feature embeddings into a low-dimensional space to better retain local information. The model’s design, particularly the decoding layer of the time-series Transformer, is optimized with a multi-head self-attentive mechanism, and multi-dimensional attention weights visualization methods are employed to clarify the fault feature extraction process. Quantitative visualizations throughout training improve interpretability and insight into learning dynamics. Experimental results indicate that this model surpasses state-of-the-art approaches on benchmark datasets, proving its generalizability and robustness in diverse testing scenarios. [ABSTRACT FROM AUTHOR]

In this article, we study a machine learning-based approach for characterising crack-like defects in specimens with complex geometries using the multi-modal total focusing method (M-TFM) applied to ultrasonic full matrix capture (FMC) data. In particular, this work frames into a scientific machine learning perspective leveraging the use of physics-based numerical solvers as an efficient yet accurate data augmentation strategy. Toward this end, the numerical simulations have been tailored to built a suitable data set aiming at gathering the uncertainties in M-TFM images encountered when the reconstruction parameters are barely known. That is, the use of such a physical knowledge has been exploited to train a deep convolutional neural network designed for retrieving crack size and position based on multi-modal TFM images. Furthermore, for validating the learning proposed scheme, an experimental campaign has been also carried out accordingly to the most relevant factors that typically impact the quality of reconstructed M-TFM images. The developed approach has been tested on both synthetic and experimental data sets never shared in the training phase. [ABSTRACT FROM AUTHOR]

This study contributes to the fields of nondestructive testing (NDT) and structural health monitoring (SHM), which are pivotal for extending the service life of infrastructure and aligning with sustainability goals in construction. The study emphasizes an advanced application of the deformation area difference (DAD) Method, focusing on the comparative analysis of curvature lines derived from photogrammetry‐assisted deflection measurements and direct strain measurements. The novel strain area difference (SAD) method is presented. The research includes numerical simulations and practical experiments. A laboratory experiment with a steel beam demonstrates the method's effectiveness by comparing curvature lines from strain and deflection data. Additionally, an on‐site application on a composite bridge using photogrammetry and drone technology is described with a comparative numerical analysis. The investigations' results shed light on the potential of integrating diverse data sources in NDT/SHM practices. For instance, the integration of deflection measurements and strain measurements for SHM. The comparative analysis provides crucial insights into the accuracy and applicability of the DAD and SAD method in various structural scenarios, improving the precision of condition assessments in reinforced concrete structures. The findings have significant implications for the sustainable maintenance and operation of infrastructure. They support efficient decision‐making in the maintenance and repair of structures, contributing to their longevity and reducing the need for extensive interventions. They present a robust framework for decision‐making in predictive maintenance. [ABSTRACT FROM AUTHOR]

China's operational highway subgrades exhibit a trend of diversifying types and an increasing number of defects, leading to more frequent urban road safety incidents. This paper starts from the non-destructive testing of urban road subgrade defects using geological radar, aiming to achieve intelligent identification of subgrade pathologies with geological radar. The GprMax forward simulation software is used to establish multi-layer composite structural models of the subgrade, studying the characteristics of geological radar images for different types of subgrade diseases. Based on the forward simulation images of geological radar for subgrade defects and field measurement data, a geological radar subgrade defect image database is established. The Faster R-CNN deep learning algorithm is applied to achieve target detection, recognition, and classification of subgrade defect images. By comparing the loss value, total number of identified regions, and recognition accuracy as metrics, the study compares four improved versions of the Faster R-CNN algorithm. The results indicate that the faster_rcnn_inception_v2 version is more suitable for the intelligent identification of non-destructive testing of urban road subgrade defects. [ABSTRACT FROM AUTHOR]

In nondestructive testing, magnetic flux leakage (MFL) inspection is extensively employed for the inversion of pipeline defects. Exact reconstruction of the defect plane with measurements is a pressing issue in the field of MFL detection. This article proposes a method that incorporates a layer-by-layer genetic algorithm with tabu search (GA-TS). During the iterative process, our objective is to utilize a tabu search based on evolutionary algorithms to avoid local optimal solutions, obtain global optimal solutions, and reconstruct defects with enhanced accuracy. In addition to enhancing the accuracy of pipeline defect categorization, our approach has two significant limitations: its limited global search capability and slow convergence rate. Finally, the method is evaluated via experiments in which MFL signals obtained from an experimental platform and simulation signals are used. Practical results and a thorough comparative study of alternative approaches confirm the model's superiority. [ABSTRACT FROM AUTHOR]

Stainless steel core plates (SSCPs) show great potential for modular construction due to their superiority of excellent mechanical properties, light weight, and low cost over traditional concrete and honeycomb structures. During the brazing process of SSCP joints which connect the skin panel and core tubes, it is difficult to keep an even heat flow of inert gas in the vast furnace, which can lead to partially missing solder defects in brazing joints. Pulsed eddy current imaging (PECI) has demonstrated feasibility for detecting missing solder defects, but various factors including lift-off variation and image blurring can deteriorate the quality of C-scan images, resulting in inaccurate evaluation of the actual state of the brazed joints. In this study, a differential pulsed eddy current testing (PECT) probe is designed to reduce the lift-off noise of PECT signals, and a mask-based image segmentation and thinning method is proposed to eliminate the blurring effect of C-scan images. The structure of the designed probe was optimized based on finite element simulation and the positive peak of the PECT signal was selected as the signal feature. Experiments with the aid of a scanning device are then carried out to image the interrogated regions of the SSCP specimen. The peak values of the signals were collected in a matrix to generate images of the scanned brazing joints. Results show that lift-off noise is significantly reduced by using the differential probe. Image blurring caused by the convolution effect of the probe's point spread function with the imaging object was eliminated using a mask-based image segmentation and thinning method. The restored C-scan images enhance the sharpness of the profiles of the brazing joints and the opening in the images accurately reflect the missing solder of the brazed joints. [ABSTRACT FROM AUTHOR]

Ultrasonic nondestructive testing is widely used not only in the laboratory, but also in industry. The tests use various types of ultrasonic waves, diverse measurement techniques and different apparatus. One of the problems encountered is the high susceptibility of the surface wave to interference. Some of the interference is random in nature and can be minimized (e.g., contamination of the surface or resting a finger on the surface under study). Some of the interference is permanent in nature, such as variable surface roughness. The aim of the conducted research was to evaluate the influence of roughness on ultrasonic wave propagation. The study used samples with surface roughness Sa from 0.28 to 219 µm, and ultrasonic surface wave probes with frequencies from 1.41 to 8.02 MHz. It was observed that roughness significantly affects the attenuation of the ultrasonic wave, and the differences in signal amplification reached more than 15 dB. Similarly, the effect of the ultrasonic wave's transit time through surfaces of different roughness was noted. It was found that the difference in the ultrasonic wave transition time was more than 50 µs. The results of the study can be helpful for the ultrasonic testing of materials with different surface conditions. [ABSTRACT FROM AUTHOR]

A numerical tool for simulating the detection signals of electromagnetic nondestructive testing technology (ENDT) is of great significance for studying detection mechanisms and improving detection efficiency. However, the quantitative analysis methods for ENDT have not yet been sufficiently studied due to the absence of an effective constitutive model. This paper proposed a new magneto–mechanical model that can reflect the dependence of relative permeability on elasto–plastic deformation and proposed a finite element–infinite element coupling method that can replace the traditional finite element truncation boundary. The validity of the finite element–infinite element coupling method is verified by the experimental result of testing electromagnetic analysis methods using TEAM Problem 7. Then, the reliability and accuracy of the proposed model are verified by comparing the simulation results under elasto–plastic deformation with experimental results. This paper also investigates the effect of elasto–plastic deformation on the transient magnetic flux signal, a quantitative hyperbolic tangent model between Bzpp (peak–peak value of the normal component of magnetic flux signal) and elastic stress, and the exponential function relationship between Bzpp and plastic deformation is established. In addition, the difference and mechanism of a magnetic flux signal under elasto–plastic deformations are analyzed. The results reveal that the variation of the transient magnetic flux signal is mainly due to domain wall pinning, which is significantly affected by elasto–plastic deformation. The results of this paper are important for improving the accuracy of quantitative ENDT for elasto–plastic deformation. [ABSTRACT FROM AUTHOR]

Non-destructive testing (NDT) enables the determination of internal defects and flaws in concrete structures without damaging them, making it a common application in current bridge concrete inspections. However, due to the complexity of the internal structure of this type of concrete, limitations regarding measurement point placement, and the extensive detection area, accurate defect detection cannot be guaranteed. This paper proposes a method that combines the Simultaneous Algebraic Reconstruction Technique with Group Sparsity Regularization (SART-GSR) to achieve tomographic imaging of bridge concrete under sparse measurement conditions. Firstly, a mathematical model is established based on the principles of the tomographic imaging of bridge concrete; secondly, the SART algorithm is used to solve for its velocity values; thirdly, on the basis of the SART results, GSR is applied for optimized solution processing; finally, simulation experiments are conducted to verify the reconstruction effects of the SART-GSR algorithm compared with those of the SART and ART algorithms. The results show that the SART-GSR algorithm reduced the relative error to 1.5% and the root mean square error to 89.76 m/s compared to the SART and ART algorithms. This improvement in accuracy makes it valuable for the tomographic imaging of bridge concrete and provides a reference for defect detection in bridge concrete. [ABSTRACT FROM AUTHOR]

The nutritional quality of bananas and their geographical origin authenticity are very important for trade. There is an urgent need for rapid, non-destructive testing to improve the origin and quality assurance for importers, distributors, and consumers. In this study, 99 banana samples from a range of producing countries were collected. Hyperspectral data were combined with chemometric methods to construct quantitative and qualitative models for bananas, predicting soluble solids content (SSC), potassium content (K), and country of origin. A second derivative analysis combined with competitive adaptive weighted sampling (CARS) and random frog jumping (RF) was selected as the best pre-treatment method for the prediction of SSC and K content, respectively. Partial least squares (PLS) models achieved R2p values of 0.8012 and 0.8606 for SSC and K content, respectively. Chinese domestic and imported bananas were classified with a prediction accuracy of 95.83% using partial least squares-discriminant analysis (PLS-DA) and an RF method that screened the spectral variables after a second pretreatment. These results showed that hyperspectral imaging technology could be effectively used to non-destructively predict the nutrient contents of bananas and identify their geographical origin. In the future, this technology can be applied to determine the nutritional quality composition and geographical origin of bananas from other countries. [ABSTRACT FROM AUTHOR]

Conventional food fraud detection using hyperspectral imaging (HSI) relies on the discriminative power of machine learning. However, these approaches often assume a balanced class distribution in an ideal laboratory environment, which is impractical in real-world scenarios with diverse label distributions. This results in suboptimal performance when less frequent classes are overshadowed by the majority class during training. Thus, the critical research challenge emerges of how to develop an effective classifier on a small-scale imbalanced dataset without significant bias from the dominant class. In this paper, we propose a novel nondestructive detection approach, which we call the Dice Loss Improved Self-Supervised Learning-Based Prototypical Network (Proto-DS), designed to address this imbalanced learning challenge. The proposed amalgamation mitigates the label bias on the most frequent class, further improving robustness. We validate our proposed method on three collected hyperspectral food image datasets with varying degrees of data imbalance: Citri Reticulatae Pericarpium (Chenpi), Chinese herbs, and coffee beans. Comparisons with state-of-the-art imbalanced learning techniques, including the Synthetic Minority Oversampling Technique (SMOTE) and class-importance reweighting, reveal our method's superiority. Notably, our experiments demonstrate that Proto-DS consistently outperforms conventional approaches, achieving the best average balanced accuracy of 88.18% across various training sample sizes, whereas the Logistic Model Tree (LMT), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN) approaches attain only 59.42%, 60.38%, and 66.34%, respectively. Overall, self-supervised learning is key to improving imbalanced learning performance and outperforms related approaches, while both prototypical networks and the Dice loss can further enhance classification performance. Intriguingly, self-supervised learning can provide complementary information to existing imbalanced learning approaches. Combining these approaches may serve as a potential solution for building effective models with limited training data. [ABSTRACT FROM AUTHOR]

Structural components with curved edges are common in many engineering designs. Fatigue cracks, corrosion and other types of defects and mechanical damage often initiate from (or are located close to) edges. Damage and defect detection in the presence of complex geometry represents a significant challenge for non-destructive testing (NDT). To address this challenge, this paper investigates the fundamental mode of the quasi-symmetric edge-guided wave ( Q E S 0 ) propagating along a curved edge, as well as its scattering characteristics in the presence of different types of edge defects. The finite element (FE) approach is used to investigate the propagation and mode shapes of the Q E S 0 . It was found that the wave attenuation dramatically increases when the radius-to-thickness ratio is less than 20. Moreover, the mode shapes are significantly affected by the waveguide curvature as well as the excitation frequency. Additionally, to evaluate the sensitivity of Q E S 0 to edge defects, different sizes of edge defects were investigated with the FE model, which validated against experimental results. The validated FE model was further employed to quantify the dependence of the amplitude of scattered waves for different types of edge defects. These studies indicate that the amplitude of scattered wave is very sensitive to the presence of edge defects. The main outcome of this work is the demonstrated ability of the Q E S 0 wave mode to propagate over long distances and a high sensitivity of this mode to different types of edge defects, which manifest its great potential for detecting and characterising damage near the curved edges of structural components. [ABSTRACT FROM AUTHOR]

The increasing variety and frequency of subgrade defects in operational highways have led to a rise in road safety incidents. This study employed ground-penetrating radar (GPR) detection and forward simulation to analyze the characteristic patterns of common subgrade defects, such as looseness, voids, and cavities. Through the integration of instantaneous feature information from different defect patterns with complex signal techniques, the boundary judgment of structural layers and anomalies in GPR images of various subgrade defects was improved. An intelligent recognition platform was established, and a radar image dataset was created and trained to evaluate the recognition performance of the You Only Look Once (YOLO) v3 and Single-Shot Multi-Box Detector (SSD) algorithms. Evaluation metrics such as precision, recall, F1-score, average precision (AP), and mean average precision (mAP) were used to assess the detection efficiency and accuracy for subgrade defect images. The results showed that YOLO v3 achieved an average detection accuracy of 76.69%, while the SSD achieved 75.07%. This study demonstrates that the reliability of the intelligent recognition and classification of highway subgrade defects can be enhanced by using GPR for non-destructive testing. [ABSTRACT FROM AUTHOR]

The robotization of the non-destructive inspection of aircraft is essential for improving the accuracy and duration of performed inspections, being an integral part of inspection and data management systems within the currently developed NDT 4.0 concept. In this paper, the authors presented the design and testing of a universal mobile platform with interchangeable sensing systems for the non-destructive inspection of aircraft structures with various angles of inclination. As a result of the performed studies, a low-cost approach of automation of existing measurement devices used for inspection was proposed. The constructed prototype of the mobile platform was equipped with eddy current testing probe and successfully passed both laboratory and environmental tests, demonstrating its performance in various conditions. The presented approach confirms the effectiveness of the automation of the inspection process using climbing robots and defining the directions of possible development of automation in non-destructive testing in aviation. [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper delves into a significant area of research, exploring the application of the standard boundary element method (BEM) to analyze inverse acoustic frequency scattering problems in 2D space using a rigid corrugated circular object. The inverse problem is reformulated as an optimization problem, with the boundary of the scatterer parametrized to reduce the number of optimization variables. The influence of these parameters on imaging results based on near-field data is examined. The analysis uses a flat wave illuminating the object along the positive x-axis direction. The study evaluates the accuracy of the solution across various parameters defining the boundary of the analyzed object. The findings, which significantly contribute to advancements in computational methods, non-destructive testing, and the understanding of functional properties of materials and structures, offer valuable insights into numerical techniques and their practical engineering applications. [ABSTRACT FROM AUTHOR]

This review examines the state‐of‐the‐art sensors and sensing technologies employed for structural health monitoring (SHM) in aerospace composites, highlighting the shift from conventional nondestructive evaluation techniques to real‐time monitoring systems. The review discusses the challenges associated with composite materials, such as their anisotropic nature and susceptibility to invisible damage, and how these challenges have driven the improvement of SHM techniques. Fiber‐optic sensors, including interferometric, distributed, and grating‐based sensors, are analyzed for their high sensitivity and multiplexing capabilities, making them suitable for distributed sensing applications. Piezoelectric sensors are evaluated for their effectiveness in both active and passive damage detection methods. At the same time, piezoresistive self‐sensing systems are explored for their potential to integrate sensing directly into composite materials. The review also addresses the challenges encountered in implementing SHM systems. It suggests solutions like protective coatings, advanced data processing algorithms, and modular system design to overcome these challenges. In conclusion, this review provides a comprehensive overview of the current SHM technologies for aerospace composites, underscoring the need for sustained research and development to improve sensor technology, expand data processing capabilities, and ensure seamless integration with aircraft systems, thus contributing to the safety and efficiency of aerospace operations. [ABSTRACT FROM AUTHOR]

Terahertz time-domain spectroscopy is a widely used non-contact detection technology with significant potential in the field of non-destructive testing. In this study, the transfer function of the coating system is derived based on the Rouard model and the refractive index of the coating within the transfer function is modelled using the Debye model. The hierarchical thickness of the multilayer composite material is fitted by random optimisation algorithm. Considering the complexity of the objective function and the practical accuracy requirements in engineering problems, the integer genetic algorithm is adopted to find the optimal parameters. The comparison of the ordinary genetic algorithm and the integer genetic algorithm reveals that the integer genetic algorithm requires a significantly smaller population size and computation time than the ordinary genetic algorithm. Furthermore, the predicted thickness obtained by the integer genetic algorithm is more repeatable, has higher computational efficiency, and exhibits smaller errors. Our results indicate that combining the integer genetic algorithm with the Rouard model holds potential applications in thickness measurement of multilayer composite materials. [ABSTRACT FROM AUTHOR]

Nowadays, composite materials have become prevalent across various sectors, particularly finding usage in large-scale applications such as spaceships, automobiles, and aircrafts. The accurate detection of the defects in these materials is crucial, yet traditional methods often rely on human inspection, which is susceptible to errors. Recent advancements in machine learning have enabled defect detection using ultrasonic non-destructive testing methods. This paper introduces a new dataset named UNDT, which is obtained from the scans of 60 different composite materials, generating a total of 1150 images depicting both defective and non-defective areas. Several transfer learning methods are applied on the newly introduced UNDT dataset as well as the publicly available USimgAIST ultrasonic dataset. Comparative performance assessments illustrate the significance of utilising the transfer learning approach for defect classification on ultrasonic inspection images. Furthermore, the research emphasises the substantial benefits of employing these transfer learning methods. Notably, the DenseNet121 and VGG19 models achieve the highest accuracy rates, with 98.8% and 98.6% on the UNDT and USimgAIST datasets, respectively. [ABSTRACT FROM AUTHOR]

Exploring a non-destructive and rapid evaluation method for precious ancient ceramic relics is of significant importance. Currently, there are countless monochrome porcelain fragments awaiting measurement and categorization. Various instruments such as XRF, XRD, SEM, OM, and thermoluminescence dating have been extensively utilized by numerous researchers to study ancient ceramics. However, these techniques pose challenges in reliably identifying monochrome porcelain fragments from the same kiln with similar appearance, content, and microstructure due to their limitations. To address this issue, this study presents an acoustic measurement system that utilizes audible frequencies to non-destructively evaluate monochrome porcelain fragments. The proposed method enables the extraction of parameters related to time domain analysis (e.g., group delay), frequency domain analysis (e.g., resonance), and sound loss characteristics of these fragments. This non-destructive and efficient technology for detecting acoustic characteristics of monochrome porcelain fragments presented in this work clarifies the fundamental principles governing the interaction between sound waves and ancient ceramic fragments while providing a completely non-destructive and highly efficient method for classifying and restoring valuable solid cultural heritages like stone, jade, bronze etc. Moreover, this approach can also be applied for non-destructive testing of elastic modulus in advanced ceramic devices including detecting small cracks, deterioration effects due to aging as well as other defects. [ABSTRACT FROM AUTHOR]

Highly accelerated lifetime test (HALT) for lifetime evaluation was performed on the state-of-art automotive MLCC prototypes by varying Dy/Mg (donor/acceptor) ratio added to BaTiO 3. The results clearly showed that the mean time to failure (MTTF) more than 280 times as the Dy/Mg ratio increased from 1.0 to 10.0. Since oxygen vacancies typically form in-gap state/defect at the donor level, the activation energy value under thermal activation process as a function of voltage was calculated and compared by non-destructive testing (I – V curve) for an accurate evaluation of the extrinsic behavior. It was found that barrier height at the Ni/BT interface decreases as the voltage increases, resulting in a decrease in activation energy. As the Dy/Mg ratio increases, the density of defects/in-gap states formed at the donor level in the bandgap by oxygen vacancies decreases, which may lead to a decrease in the number of electrons excited by the external voltage. Furthermore, it was verified that the calculated Schottky barrier height of the 10.0 Dy/Mg ratio under voltage has higher value than that of the 2.6 Dy/Mg ratio. Based on the results of this study, we propose a new indicator for the design of automotive MLCCs with high lifetime reliability that can be used for comparative analysis of additive compositions through non-destructive test (I – V curve) with low evaluation time/cost. [Display omitted] [ABSTRACT FROM AUTHOR]

The non-destructive testing for subsurface damage of processed silicon nitride ceramics is significant to the improvement of processing technology and evaluation of product performance. A novel method for the measurement of subsurface damage based on dispersion of laser-excited surface acoustics wave is proposed in this paper. The subsurface damage distribution is quantified as the damage density and depth of the damage layer. The forward model of surface acoustics wave propagation in damage layer is derived by the stiffness matrix method. Bayesian inversion is applied to estimate model parameters and uncertainties of subsurface damage from the experimental dispersion data. Laser ultrasonic experiments are carried out on four samples with different substrate material parameters and surface qualities to demonstrate the feasibility of the method for detecting varying degrees of subsurface damage. The results prove that with reasonable use of prior information about damage depth predicted by surface roughness, subsurface damage with a depth ∼10 times smaller than the minimum wavelength of surface acoustics wave is accurately characterized. The reliability of the results is further verified by Vickers microhardness tester. [ABSTRACT FROM AUTHOR]

Highlights Low‐thickness/diameter cylindrical storage pressure vessels with composite overwrapping fail at diverse internal and external pressure circumstances, causing several faults that must be included and minimized to retain effectiveness and working capability. The bursting effect is caused by hoop (internal) stresses and can be reduced by composite lamination alignment and internal pressure resistance. Pressure vessel bursting faults vary with internal tensions and can induce fiber reinforced polymer fracture and composite cylinder failure under many failure criteria. Micro‐mechanical nondestructive testing uses independent failure criteria associated to modes like Tsai‐Wu, Tsai‐Hill, Azzi‐Tsai, Hoffman, and Chamis. Micro‐cracking of composite layers generates delamination in composite pressure vessels, causing porosity. Pressure vessel micro‐cracking was assessed using classical laminate theory. Composite material strain‐rate failures in mobile applications like hydrogen storage cars with composite pressure vessels are examined due to their widespread use. Finally, Polar, Helical, and Hoop winding sequences are consistently focused to optimize the design and analyze the parameters for the overall impact of different failure aspects as bursting behavior in cylindrical composite pressure vessels. Type‐IV composite pressure vessels have the best results due to their lightest weight but complexity of winding over the polar head geodesic and non‐geodesic curvatures overwrapped carbon include all these internal composite and liner geometry faults and optimize them to reduce failure issues to fix composite overwrapped pressure vessels (COPV's) design. Liner‐less composite pressure vessels (Type‐V) need more design and production study for high‐pressure and temperature applications. Failure deals with the internal and external pressures exerted on composite pressure vessels. Failures like bursting of the pressure vessel, due to like micro‐cracking, delamination's and strain‐rate failures of composites. Failures detection through NDT inspection for polymer composite materials for composite pressure vessels. (COPV) perfect lamina sequence, liner materials and fiber winding angles must be considered for designing state of the art liner‐less Type‐V pressure vessels. Independent Failure criteria like (Tsai‐Wu & Hashin). [ABSTRACT FROM AUTHOR]