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Keywords = nondestructive inspection technology

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28 pages, 3364 KiB  
Review
Principles, Applications, and Future Evolution of Agricultural Nondestructive Testing Based on Microwaves
by Ran Tao, Leijun Xu, Xue Bai and Jianfeng Chen
Sensors 2025, 25(15), 4783; https://doi.org/10.3390/s25154783 - 3 Aug 2025
Viewed by 56
Abstract
Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness [...] Read more.
Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article
(This article belongs to the Section Smart Agriculture)
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29 pages, 4633 KiB  
Article
Failure Detection of Laser Welding Seam for Electric Automotive Brake Joints Based on Image Feature Extraction
by Diqing Fan, Chenjiang Yu, Ling Sha, Haifeng Zhang and Xintian Liu
Machines 2025, 13(7), 616; https://doi.org/10.3390/machines13070616 - 17 Jul 2025
Viewed by 254
Abstract
As a key component in the hydraulic brake system of automobiles, the brake joint directly affects the braking performance and driving safety of the vehicle. Therefore, improving the quality of brake joints is crucial. During the processing, due to the complexity of the [...] Read more.
As a key component in the hydraulic brake system of automobiles, the brake joint directly affects the braking performance and driving safety of the vehicle. Therefore, improving the quality of brake joints is crucial. During the processing, due to the complexity of the welding material and welding process, the weld seam is prone to various defects such as cracks, pores, undercutting, and incomplete fusion, which can weaken the joint and even lead to product failure. Traditional weld seam detection methods include destructive testing and non-destructive testing; however, destructive testing has high costs and long cycles, and non-destructive testing, such as radiographic testing and ultrasonic testing, also have problems such as high consumable costs, slow detection speed, or high requirements for operator experience. In response to these challenges, this article proposes a defect detection and classification method for laser welding seams of automotive brake joints based on machine vision inspection technology. Laser-welded automotive brake joints are subjected to weld defect detection and classification, and image processing algorithms are optimized to improve the accuracy of detection and failure analysis by utilizing the high efficiency, low cost, flexibility, and automation advantages of machine vision technology. This article first analyzes the common types of weld defects in laser welding of automotive brake joints, including craters, holes, and nibbling, and explores the causes and characteristics of these defects. Then, an image processing algorithm suitable for laser welding of automotive brake joints was studied, including pre-processing steps such as image smoothing, image enhancement, threshold segmentation, and morphological processing, to extract feature parameters of weld defects. On this basis, a welding seam defect detection and classification system based on the cascade classifier and AdaBoost algorithm was designed, and efficient recognition and classification of welding seam defects were achieved by training the cascade classifier. The results show that the system can accurately identify and distinguish pits, holes, and undercutting defects in welds, with an average classification accuracy of over 90%. The detection and recognition rate of pit defects reaches 100%, and the detection accuracy of undercutting defects is 92.6%. And the overall missed detection rate is less than 3%, with both the missed detection rate and false detection rate for pit defects being 0%. The average detection time for each image is 0.24 s, meeting the real-time requirements of industrial automation. Compared with infrared and ultrasonic detection methods, the proposed machine-vision-based detection system has significant advantages in detection speed, surface defect recognition accuracy, and industrial adaptability. This provides an efficient and accurate solution for laser welding defect detection of automotive brake joints. Full article
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28 pages, 1536 KiB  
Review
Remote Non-Destructive Testing of Port Cranes: A Review of Vibration and Acoustic Sensors with IoT Integration
by Jan Lean Tai, Mohamed Thariq Hameed Sultan, Rafał Grzejda and Farah Syazwani Shahar
J. Mar. Sci. Eng. 2025, 13(7), 1338; https://doi.org/10.3390/jmse13071338 - 13 Jul 2025
Viewed by 599
Abstract
Safe and efficient operation of port cranes is vital for maintaining the efficiency of global maritime logistics. However, traditional non-destructive testing methods face significant limitations in harsh port environments, such as periodic inspection intervals, restricted access to structural components, and a lack of [...] Read more.
Safe and efficient operation of port cranes is vital for maintaining the efficiency of global maritime logistics. However, traditional non-destructive testing methods face significant limitations in harsh port environments, such as periodic inspection intervals, restricted access to structural components, and a lack of real-time monitoring. This review explores the emerging paradigm of remote non-destructive testing through the integration of vibration and acoustic emission sensors with Internet of Things platforms. By enabling continuous, real-time monitoring, these sensor systems can detect early indicators of mechanical degradation, structural fatigue, and corrosion. This study synthesizes findings from over 100 peer-reviewed sources and identifies a significant gap in the application of these technologies to port cranes. Although vibration and acoustic emission sensors have been widely studied in various fields, their application to port cranes remains underexplored, presenting a novel and promising avenue for future research and practical applications. The unique operational demands and structural complexities of port cranes, coupled with their critical role in global trade logistics, make them ideal for leveraging these sensors in tandem with Internet of Things solutions. This integration not only overcomes the limitations of traditional non-destructive testing methods, but also offers substantial benefits, including enhanced safety, reduced inspection costs, and improved operational efficiency. This review concludes by proposing future research directions to enhance sensor performance, data analytics, and Internet of Things integration, paving the way for predictive maintenance strategies that increase operational uptime and improve safety in port crane operations. Full article
(This article belongs to the Section Ocean Engineering)
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20 pages, 6286 KiB  
Article
Near-Field Microwave Sensing for Chip-Level Tamper Detection
by Maryam Saadat Safa and Shahin Tajik
Sensors 2025, 25(13), 4188; https://doi.org/10.3390/s25134188 - 5 Jul 2025
Viewed by 382
Abstract
Stealthy chip-level tamper attacks, such as hardware Trojan insertions or security-critical circuit modifications, can threaten modern microelectronic systems’ security. While traditional inspection and side-channel methods offer potential for tamper detection, they may not reliably detect all forms of attacks and often face practical [...] Read more.
Stealthy chip-level tamper attacks, such as hardware Trojan insertions or security-critical circuit modifications, can threaten modern microelectronic systems’ security. While traditional inspection and side-channel methods offer potential for tamper detection, they may not reliably detect all forms of attacks and often face practical limitations in terms of scalability, accuracy, or applicability. This work introduces a non-invasive, contactless tamper detection method employing a complementary split-ring resonator (CSRR). CSRRs, which are typically deployed for non-destructive material characterization, can be placed on the surface of the chip’s package to detect subtle variations in the impedance of the chip’s power delivery network (PDN) caused by tampering. The changes in the PDN’s impedance profile perturb the local electric near field and consequently affect the sensor’s impedance. These changes manifest as measurable variations in the sensor’s scattering parameters. By monitoring these variations, our approach enables robust and cost-effective physical integrity verification requiring neither physical contact with the chips or printed circuit board (PCB) nor activation of the underlying malicious circuits. To validate our claims, we demonstrate the detection of various chip-level tamper events on an FPGA manufactured with 28 nm technology. Full article
(This article belongs to the Special Issue Sensors in Hardware Security)
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36 pages, 1925 KiB  
Review
Deep Learning-Enhanced Spectroscopic Technologies for Food Quality Assessment: Convergence and Emerging Frontiers
by Zhichen Lun, Xiaohong Wu, Jiajun Dong and Bin Wu
Foods 2025, 14(13), 2350; https://doi.org/10.3390/foods14132350 - 2 Jul 2025
Viewed by 1420
Abstract
Nowadays, the development of the food industry and economic recovery have driven escalating consumer demands for high-quality, nutritious, and safe food products, and spectroscopic technologies are increasingly prominent as essential tools for food quality inspection. Concurrently, the rapid rise of artificial intelligence (AI) [...] Read more.
Nowadays, the development of the food industry and economic recovery have driven escalating consumer demands for high-quality, nutritious, and safe food products, and spectroscopic technologies are increasingly prominent as essential tools for food quality inspection. Concurrently, the rapid rise of artificial intelligence (AI) has created new opportunities for food quality detection. As a critical branch of AI, deep learning synergizes with spectroscopic technologies to enhance spectral data processing accuracy, enable real-time decision making, and address challenges from complex matrices and spectral noise. This review summarizes six cutting-edge nondestructive spectroscopic and imaging technologies, near-infrared/mid-infrared spectroscopy, Raman spectroscopy, fluorescence spectroscopy, hyperspectral imaging (spanning the UV, visible, and NIR regions, to simultaneously capture both spatial distribution and spectral signatures of sample constituents), terahertz spectroscopy, and nuclear magnetic resonance (NMR), along with their transformative applications. We systematically elucidate the fundamental principles and distinctive merits of each technological approach, with a particular focus on their deep learning-based integration with spectral fusion techniques and hybrid spectral-heterogeneous fusion methodologies. Our analysis reveals that the synergy between spectroscopic technologies and deep learning demonstrates unparalleled superiority in speed, precision, and non-invasiveness. Future research should prioritize three directions: multimodal integration of spectroscopic technologies, edge computing in portable devices, and AI-driven applications, ultimately establishing a high-precision and sustainable food quality inspection system spanning from production to consumption. Full article
(This article belongs to the Section Food Quality and Safety)
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31 pages, 6682 KiB  
Review
Research Progress on Non-Destructive Testing Technology and Equipment for Poultry Eggshell Quality
by Qiaohua Wang, Zheng Yang, Chengkang Liu, Rongqian Sun and Shuai Yue
Foods 2025, 14(13), 2223; https://doi.org/10.3390/foods14132223 - 24 Jun 2025
Viewed by 510
Abstract
Eggshell quality inspection plays a pivotal role in enhancing the commercial value of poultry eggs and ensuring their safety. It effectively enables the screening of high-quality eggs to meet consumer demand for premium egg products. This paper analyzes the surface characteristics, ultrastructure, and [...] Read more.
Eggshell quality inspection plays a pivotal role in enhancing the commercial value of poultry eggs and ensuring their safety. It effectively enables the screening of high-quality eggs to meet consumer demand for premium egg products. This paper analyzes the surface characteristics, ultrastructure, and mechanical properties of poultry eggshells. It systematically reviews current advances in eggshell quality inspection technologies and compares the suitability and performance of techniques for key indicators, including shell strength, thickness, spots, color, and cracks. Furthermore, the paper discusses challenges in non-destructive testing, including individual egg variations, species differences, hardware precision limitations, and inherent methodological constraints. It summarizes commercially available portable and online non-destructive testing equipment, analyzing core challenges: the cost–accessibility paradox, speed–accuracy trade-off, algorithm interference impacts, and the technology–practice gap. Additionally, the paper explores the potential application of several emerging technologies—such as tactile sensing, X-ray imaging, laser-induced breakdown spectroscopy, and fluorescence spectroscopy—in eggshell quality inspection. Finally, it provides a comprehensive outlook on future research directions, offering constructive guidance for subsequent studies and practical applications in production. Full article
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15 pages, 4137 KiB  
Article
Non-Destructive Thickness Measurement of Energy Storage Electrodes via Terahertz Technology
by Zhengxian Gao, Xiaoqing Jia, Jin Wang, Zhijun Zhou, Jianyong Wang, Dongshan Wei, Xuecou Tu, Lin Kang, Jian Chen, Dengzhi Chen and Peiheng Wu
Sensors 2025, 25(13), 3917; https://doi.org/10.3390/s25133917 - 23 Jun 2025
Viewed by 435
Abstract
Precision thickness control in new energy electrode coatings is a critical determinant of battery performance characteristics. This study presents a non-destructive inspection methodology employing terahertz time-domain spectroscopy (THz-TDS) to achieve high-precision coating thickness measurement in lithium iron phosphate (LFP) battery manufacturing. Industrial THz-TDS [...] Read more.
Precision thickness control in new energy electrode coatings is a critical determinant of battery performance characteristics. This study presents a non-destructive inspection methodology employing terahertz time-domain spectroscopy (THz-TDS) to achieve high-precision coating thickness measurement in lithium iron phosphate (LFP) battery manufacturing. Industrial THz-TDS systems mostly adopt fixed threshold filtering or Fourier filtering, making it disssssfficult to balance noise suppression and signal fidelity. The developed approach integrates three key technological advancements. Firstly, the refractive index of the material is determined through multi-peak amplitude analysis, achieving an error rate control within 1%. Secondly, a hybrid signal processing algorithm is applied, combining an optimized Savitzky–Golay filter for high-frequency noise suppression with an enhanced sinc function wavelet threshold technique for signal fidelity improvement. Thirdly, the time-of-flight method enables real-time online measurement of coating thickness under atmospheric conditions. Experimental validation demonstrates effective thickness measurement across a 35–425 μm range, achieving a 17.62% range extension and a 2.13% improvement in accuracy compared to conventional non-filtered methods. The integrated system offers a robust quality control solution for next-generation battery production lines. Full article
(This article belongs to the Section Physical Sensors)
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46 pages, 6097 KiB  
Review
Recent Advances and Applications of Imaging and Spectroscopy Technologies for Tea Quality Assessment: A Review
by Shujun Zhi, Ting An, Han Zhang, Yuhao Bai, Baohua Zhang and Guangzhao Tian
Agronomy 2025, 15(7), 1507; https://doi.org/10.3390/agronomy15071507 - 21 Jun 2025
Viewed by 623
Abstract
Significant research has been carried out on the applications of imaging and spectroscopy technologies for a variety of foods and agricultural products, and the technical fundamentals and their feasibilities have also been widely demonstrated in the past decade. Imaging technologies, including computer vision, [...] Read more.
Significant research has been carried out on the applications of imaging and spectroscopy technologies for a variety of foods and agricultural products, and the technical fundamentals and their feasibilities have also been widely demonstrated in the past decade. Imaging technologies, including computer vision, Raman, X-ray, magnetic resonance (MR), fluorescence imaging, spectroscopy technology, as well as spectral imaging technologies, including hyperspectral or multi-spectral imaging, have found their applications in non-destructive tea quality assessment. Tea quality can be assessed by considering their external qualities (color, texture, shape, and defect), internal qualities (contents of polyphenols, amino acids, caffeine, theaflavin, etc.), and safety. In recent years, numerous studies have been published to advance non-destructive methods for assessing tea quality using imaging and spectroscopy technologies. This review aims to give a thorough overview of imaging and spectroscopy technologies, data processing and analyzing methods, as well as their applications in tea quality non-destructive assessment. The challenges and directions of tea quality inspection by using imaging and spectroscopy technologies for future research and development will also be reported and formulated in this review. Full article
(This article belongs to the Section Precision and Digital Agriculture)
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24 pages, 5461 KiB  
Article
Classification and Prediction of Unknown Thermal Barrier Coating Thickness Based on Hybrid Machine Learning and Terahertz Nondestructive Characterization
by Zhou Xu, Jianfei Xu, Yiwen Wu, Changdong Yin, Suqin Chen, Qiang Liu, Xin Ge, Luanfei Wan and Dongdong Ye
Coatings 2025, 15(6), 725; https://doi.org/10.3390/coatings15060725 - 17 Jun 2025
Viewed by 475
Abstract
Thickness inspection of thermal barrier coatings is crucial to safeguard the reliability of high-temperature components of aero-engines, but traditional destructive inspection methods are difficult to meet the demand for rapid assessment in the field. In this study, a new non-destructive testing method integrating [...] Read more.
Thickness inspection of thermal barrier coatings is crucial to safeguard the reliability of high-temperature components of aero-engines, but traditional destructive inspection methods are difficult to meet the demand for rapid assessment in the field. In this study, a new non-destructive testing method integrating terahertz time-domain spectroscopy and machine learning algorithms is proposed to systematically study the thickness inspection of 8YSZ coatings prepared by two processes, namely atmospheric plasma spraying (APS) and electron beam physical vapor deposition (EB-PVD). By optimizing the preparation process parameters, 620 sets of specimens with thicknesses of 100–400 μm are prepared, and three types of characteristic parameters, namely, time delay Δt, frequency shift Δf, and energy decay η, are extracted by combining wavelet threshold denoising and time-frequency joint analysis. A CNN-RF cascade model is constructed to realize coating process classification, and an attention-LSTM and SVR weighted fusion model is developed for thickness regression prediction. The results show that the multimodal feature fusion reduces the root-mean-square error of thickness prediction to 8.9 μm, which further improves the accuracy over the single feature model. The classification accuracy reaches 96.8%, of which the feature importance of time delay Δt accounts for 62%. The hierarchical modeling strategy reduces the detection mean absolute error from 6.2 μm to 4.1 μm. the method provides a high-precision solution for intelligent quality assessment of thermal barrier coatings, which is of great significance in promoting the progress of intelligent manufacturing technology for high-end equipment. Full article
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22 pages, 5073 KiB  
Article
Deep Learning-Assisted Microscopic Polarization Inspection of Micro-Nano Damage Precursors: Automatic, Non-Destructive Metrology for Additive Manufacturing Devices
by Dingkang Li, Xing Peng, Zhenfeng Ye, Hongbing Cao, Bo Wang, Xinjie Zhao and Feng Shi
Nanomaterials 2025, 15(11), 821; https://doi.org/10.3390/nano15110821 - 29 May 2025
Viewed by 400
Abstract
Additive Manufacturing (AM), as a revolutionary breakthrough in advanced manufacturing paradigms, leverages its unique layer-by-layer construction advantage to exhibit significant technological superiority in the fabrication of complex structural components for aerospace, biomedical, and other fields. However, when addressing industrial-grade precision manufacturing requirements, key [...] Read more.
Additive Manufacturing (AM), as a revolutionary breakthrough in advanced manufacturing paradigms, leverages its unique layer-by-layer construction advantage to exhibit significant technological superiority in the fabrication of complex structural components for aerospace, biomedical, and other fields. However, when addressing industrial-grade precision manufacturing requirements, key challenges such as the multi-scale characteristics of surface damage precursors, interference from background noise, and the scarcity of high-quality training samples severely constrain the intelligent transformation of AM quality monitoring systems. This study proposes an innovative microscopic polarization YOLOv11-LSF intelligent inspection framework, which establishes an automated non-destructive testing methodology for AM device micro-nano damage precursors through triple technological innovations, effectively breaking through existing technical bottlenecks. Firstly, a multi-scale perception module is constructed based on the Large Separable Kernel Attention mechanism, significantly enhancing the network’s feature detection capability in complex industrial scenarios. Secondly, the cross-level local network VoV-GSCSP module is designed utilizing GSConv and a one-time aggregation method, resulting in a Slim-neck architecture that significantly reduces model complexity without compromising accuracy. Thirdly, an innovative simulation strategy incorporating physical features for damage precursors is proposed, constructing a virtual and real integrated training sample library and breaking away from traditional deep learning reliance on large-scale labeled data. Experimental results demonstrate that compared to the baseline model, the accuracy (P) of the YOLOv11-LSF model is increased by 1.6%, recall (R) by 1.6%, mAP50 by 1.5%, and mAP50-95 by 2.8%. The model hits an impressive detection accuracy of 99% for porosity-related micro-nano damage precursors and remains at 94% for cracks. Its unique small sample adaptation capability and robustness under complex conditions provide a reliable technical solution for industrial-grade AM quality monitoring. This research advances smart manufacturing quality innovation and enables cross-scale micro-nano damage inspection in advanced manufacturing. Full article
(This article belongs to the Section Nanofabrication and Nanomanufacturing)
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16 pages, 7622 KiB  
Review
A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
by Zhenglong Lv, Zhexin Hao, Yuhan Zhu and Cong Lu
Appl. Sci. 2025, 15(11), 6112; https://doi.org/10.3390/app15116112 - 29 May 2025
Viewed by 916
Abstract
The global expansion of road networks and the aging of infrastructure have intensified the need for efficient pavement distress detection technologies to ensure road safety and sustainability. While traditional manual inspections are time consuming and labor-intensive, recent advances in automated systems have improved [...] Read more.
The global expansion of road networks and the aging of infrastructure have intensified the need for efficient pavement distress detection technologies to ensure road safety and sustainability. While traditional manual inspections are time consuming and labor-intensive, recent advances in automated systems have improved detection precision. However, challenges persist, including limited accuracy, poor generalization across datasets, and high computational demands for pixel-level segmentation. This review systematically examines the evolution of pavement distress detection, covering three key phases: manual inspection, semi-automated systems, and non-destructive automated methods. We analyze advancements in image acquisition (e.g., 2D to 3D, ground to aerial platforms) and processing techniques (e.g., threshold-based segmentation to deep learning), highlighting critical trade-offs between speed, accuracy, and scalability. Our findings reveal that, while modern systems excel in controlled environments, their real-world performance remains inconsistent due to varying imaging conditions and underrepresented distress types. To address these gaps, we propose four future directions: (1) enhancing environmental adaptability through multi-sensor datasets, (2) optimizing datasets via self-supervised learning, (3) deploying lightweight models on edge devices for real-time analysis, and (4) integrating predictive maintenance frameworks. These strategies aim to shift pavement management from reactive repairs to proactive, data-driven decision making, ultimately supporting smarter infrastructure systems. Full article
(This article belongs to the Section Transportation and Future Mobility)
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34 pages, 1157 KiB  
Review
Advanced Non-Destructive Testing Simulation and Modeling Approaches for Fiber-Reinforced Polymer Pipes: A Review
by Jan Lean Tai, Mohamed Thariq Hameed Sultan, Andrzej Łukaszewicz, Jerzy Józwik, Zbigniew Oksiuta and Farah Syazwani Shahar
Materials 2025, 18(11), 2466; https://doi.org/10.3390/ma18112466 - 24 May 2025
Cited by 1 | Viewed by 753
Abstract
Fiber-reinforced polymer (FRP) pipes have emerged as a preferred alternative to conventional metallic piping systems in various industries, including chemical processing, marine, and oil and gas industries, owing to their superior corrosion resistance, high strength-to-weight ratio, and extended service life. However, ensuring the [...] Read more.
Fiber-reinforced polymer (FRP) pipes have emerged as a preferred alternative to conventional metallic piping systems in various industries, including chemical processing, marine, and oil and gas industries, owing to their superior corrosion resistance, high strength-to-weight ratio, and extended service life. However, ensuring the long-term reliability and structural integrity of FRP pipes presents significant challenges, primarily because of their anisotropic and heterogeneous nature, which complicates defect detection and characterization. Traditional non-destructive testing (NDT) methods, which are widely applied, often fail to address these complexities, necessitating the adoption of advanced digital techniques. This review systematically examines recent advancements in digital NDT approaches with a particular focus on their application to composite materials. Drawing from 140 peer-reviewed articles published between 2016 and 2024, this review highlights the role of numerical modeling, simulation, machine learning (ML), and deep learning (DL) in enhancing defect detection sensitivity, automating data interpretation, and supporting predictive maintenance strategies. Numerical techniques, such as the finite element method (FEM) and Monte Carlo simulations, have been shown to improve inspection reliability through virtual defect modeling and parameter optimization. Meanwhile, ML and DL algorithms demonstrate transformative capabilities in automating defect classification, segmentation, and severity assessment, significantly reducing the inspection time and human dependency. Despite these promising developments, this review identifies a critical gap in the field: the limited translation of advanced digital methods into field-deployable solutions specifically tailored for FRP piping systems. The unique structural complexities and operational demands of FRP pipes require dedicated research for the development of validated digital models, application-specific datasets, and industry-aligned evaluation protocols. This review provides strategic insights and future research directions aimed at bridging the gap and promoting the integration of digital NDT technologies into real-world FRP pipe inspection and lifecycle management frameworks. Full article
(This article belongs to the Special Issue Modeling and Optimization of Material Properties and Characteristics)
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18 pages, 3003 KiB  
Article
Performance Evaluation of AML Equipment for Determining the Depth and Location of Subsurface Facilities in South Korea
by Seung-Jun Lee and Hong-Sik Yun
Appl. Sci. 2025, 15(11), 5794; https://doi.org/10.3390/app15115794 - 22 May 2025
Viewed by 489
Abstract
The accurate detection and mapping of subsurface utilities are critical for ensuring safety and efficiency in excavation and construction projects. Among various technologies, Ground-Penetrating Radar (GPR) has been widely used for locating underground infrastructure due to its non-destructive nature and ability to detect [...] Read more.
The accurate detection and mapping of subsurface utilities are critical for ensuring safety and efficiency in excavation and construction projects. Among various technologies, Ground-Penetrating Radar (GPR) has been widely used for locating underground infrastructure due to its non-destructive nature and ability to detect both metallic and non-metallic materials. However, many GPR systems struggle to meet the practical depth requirements in real-world conditions, especially when identifying non-metallic facilities such as PVC and PE pipes. In South Korea, the legal regulations require underground utility locators to meet specific accuracy standards, including a minimum detectable depth of 3 m. These regulations also mandate periodic performance testing of surveying equipment at authorized inspection centers. Despite this, most GPR systems tested at the official performance evaluation site at Sungkyunkwan University demonstrated limited effectiveness, with an average detection range of only 1.5 m. This study evaluates the performance of a handheld All Materials Locator (AML) device developed by SubSurface Instruments, Inc., (Janesville, WI, USA) which uses ultra-high radio frequencies to detect subsurface density variations. Experimental results from both the certified test facility and field conditions indicate that the AML meets South Korea’s legal requirements for minimum depth and accuracy, by successfully identifying a wide range of subsurface utilities including non-metallic materials. The findings suggest that the AML is a viable alternative to conventional GPR systems for utility detection in regulated environments. Full article
(This article belongs to the Special Issue Ground Penetrating Radar (GPR): Theory, Methods and Applications)
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28 pages, 4904 KiB  
Review
Nondestructive Testing of Externally Bonded FRP Concrete Structures: A Comprehensive Review
by Eyad Alsuhaibani
Polymers 2025, 17(9), 1284; https://doi.org/10.3390/polym17091284 - 7 May 2025
Cited by 1 | Viewed by 988
Abstract
The growing application of Fiber-Reinforced Polymer (FRP) composites in rehabilitating deteriorating concrete infrastructure underscores the need for reliable, cost-effective, and automated nondestructive testing (NDT) methods. This review provides a comprehensive analysis of existing and emerging NDT techniques used to assess externally bonded FRP [...] Read more.
The growing application of Fiber-Reinforced Polymer (FRP) composites in rehabilitating deteriorating concrete infrastructure underscores the need for reliable, cost-effective, and automated nondestructive testing (NDT) methods. This review provides a comprehensive analysis of existing and emerging NDT techniques used to assess externally bonded FRP (EB-FRP) systems, emphasizing their accuracy, limitations, and practicality. Various NDT methods, including Ground-Penetrating Radar (GPR), Phased Array Ultrasonic Testing (PAUT), Infrared Thermography (IRT), Acoustic Emission (AE), and Impact–Echo (IE), are critically evaluated in terms of their effectiveness in detecting debonding, voids, delaminations, and other defects. Recent technological advancements, particularly the integration of artificial intelligence (AI) and machine learning (ML) in NDT applications, have significantly improved defect characterization, automated inspections, and real-time data analysis. This review highlights AI-driven NDT approaches such as automated crack detection, hybrid NDT frameworks, and drone-assisted thermographic inspections, which enhance accuracy and efficiency in large-scale infrastructure assessments. Additionally, economic considerations and cost–performance trade-offs are analyzed, addressing the feasibility of different NDT methods in real-world FRP-strengthened structures. Finally, the review identifies key research gaps, including the need for standardization in FRP-NDT applications, AI-enhanced defect quantification, and hybrid inspection techniques. By consolidating state-of-the-art research and emerging innovations, this paper serves as a valuable resource for engineers, researchers, and practitioners involved in the assessment, monitoring, and maintenance of FRP-strengthened concrete structures. Full article
(This article belongs to the Section Polymer Processing and Engineering)
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42 pages, 3137 KiB  
Review
Preventing Catastrophic Failures: A Review of Applying Acoustic Emission Testing in Multi-Bolted Flanges
by Jan Lean Tai, Mohamed Thariq Hameed Sultan, Andrzej Łukaszewicz, Zbigniew Siemiątkowski, Grzegorz Skorulski and Farah Syazwani Shahar
Metals 2025, 15(4), 438; https://doi.org/10.3390/met15040438 - 14 Apr 2025
Cited by 2 | Viewed by 1194
Abstract
The integrity of multi-bolted flanges is crucial for ensuring safety and operational efficiency in industrial systems across sectors such as oil and gas, chemical processing, and water treatment. Traditional non-destructive testing (NDT) methods often require operational downtime and may lack sensitivity for early-stage [...] Read more.
The integrity of multi-bolted flanges is crucial for ensuring safety and operational efficiency in industrial systems across sectors such as oil and gas, chemical processing, and water treatment. Traditional non-destructive testing (NDT) methods often require operational downtime and may lack sensitivity for early-stage defect detection. This review examines acoustic emission testing (AET), a real-time monitoring technique for detecting acoustic waves generated by material defects. An analysis of 145 studies demonstrated AET’s effectiveness in detecting early-stage defects across various materials and industrial applications. Recent advances in sensor technology and signal processing have significantly enhanced AET’s capabilities. However, challenges remain regarding environmental noise interference and the need for specialized expertise. The review identifies knowledge gaps and proposes future research directions, including planned laboratory experiments to characterize defect signals in multi-bolted flange systems under different operational conditions. The findings position AET as a transformative solution for industrial inspection and maintenance, offering enhanced safety and reliability for critical infrastructures. Full article
(This article belongs to the Special Issue Nondestructive Testing Methods for Metallic Material)
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