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\"This book introduces a variety of non-destructive testing (NDT) methods, including testing and application cases. New ultrasonic testing technology for complex workpieces is proposed\"-- Provided by publisher.

Pipelines play an important role in the national/international transportation of natural gas, petroleum products, and other energy resources. Pipelines are set up in different environments and consequently suffer various damage challenges, such as environmental electrochemical reaction, welding defects, and external force damage, etc. Defects like metal loss, pitting, and cracks destroy the pipeline’s integrity and cause serious safety issues. This should be prevented before it occurs to ensure the safe operation of the pipeline. In recent years, different non-destructive testing (NDT) methods have been developed for in-line pipeline inspection. These are magnetic flux leakage (MFL) testing, ultrasonic testing (UT), electromagnetic acoustic technology (EMAT), eddy current testing (EC). Single modality or different kinds of integrated NDT system named Pipeline Inspection Gauge (PIG) or un-piggable robotic inspection systems have been developed. Moreover, data management in conjunction with historic data for condition-based pipeline maintenance becomes important as well. In this study, various inspection methods in association with non-destructive testing are investigated. The state of the art of PIGs, un-piggable robots, as well as instrumental applications, are systematically compared. Furthermore, data models and management are utilized for defect quantification, classification, failure prediction and maintenance. Finally, the challenges, problems, and development trends of pipeline inspection as well as data management are derived and discussed.

Terahertz (THz) waves, whose frequencies range between microwave and infrared, are part of the electromagnetic spectrum. A gap exists in THz literature because investigating THz waves is difficult due to the weak characteristics of the waves and the lack of suitable THz sources and detectors. Recently, THz nondestructive testing (NDT) technology has become an interesting topic. This review outlines several typical THz devices and systems and engineering applications of THz NDT techniques in composite materials, thermal barrier coatings, car paint films, marine protective coatings, and pharmaceutical tablet coatings. THz imaging has higher resolution but lower penetration than ultrasound imaging. This review presents the significance and advantages provided by the emerging THz NDT technique.

Material failure may occur in a variety of situations dependent on stress conditions, temperature, and internal or external load conditions. Many of the latest engineered materials combine several material types i.e., metals, carbon, glass, resins, adhesives, heterogeneous and nanomaterials (organic/inorganic) to produce multilayered, multifaceted structures that may fail in ductile, brittle, or both cases. Mechanical testing is a standard and basic component of any design and fabricating process. Mechanical testing also plays a vital role in maintaining cost-effectiveness in innovative advancement and predominance. Destructive tests include tensile testing, chemical analysis, hardness testing, fatigue testing, creep testing, shear testing, impact testing, stress rapture testing, fastener testing, residual stress measurement, and XRD. These tests can damage the molecular arrangement and even the microstructure of engineered materials. Nondestructive testing methods evaluate component/material/object quality without damaging the sample integrity. This review outlines advanced nondestructive techniques and explains predominantly used nondestructive techniques with respect to their applications, limitations, and advantages. The literature was further analyzed regarding experimental developments, data acquisition systems, and technologically upgraded accessory components. Additionally, the various combinations of methods applied for several types of material defects are reported. The ultimate goal of this review paper is to explain advanced nondestructive testing (NDT) techniques/tests, which are comprised of notable research work reporting evolved affordable systems with fast, precise, and repeatable systems with high accuracy for both experimental and data acquisition techniques. Furthermore, these advanced NDT approaches were assessed for their potential implementation at the industrial level for faster, more accurate, and secure operations.

Unidirectional ultrasonic guided waves (UGWs) play a crucial role in the nondestructive testing and evaluation (NDT&E) domains, offering unique advantages in detecting material defects, evaluating structural integrity, and improving the accuracy of thickness measurements. This review paper thoroughly studies the state of the art of unidirectional UGWs before presenting a comprehensive review of the foundational mathematical principles of unidirectional UGWs, focusing on the recent advancements in their methodologies and applications. This review introduces ultrasonic guided waves and their modes before looking at mode excitability and selectivity, signal excitation, and mechanisms used to generate and receive guided waves unidirectionally. This paper outlines the applications of unidirectional UGWs to reflect their effectiveness, for instance, in measuring thickness and in identifying defects such as cracks and corrosion in pipelines, etc. The paper also studies the challenges associated with unidirectional UGW generation and utilisation, such as multi-mode and side lobes. It includes a review of the literature to mitigate these challenges. Finally, this paper highlights promising future perspectives and develops directions for the technique. This review aims to create a useful resource for researchers and practitioners to comprehend unidirectional ultrasonic guided waves’ capabilities, challenges, and prospects in NDT&E applications.

In view of the advantage of cross-polarized inspection (CrPI) in microwave nondestructive testing (MNT), in this paper a dual-polarized narrow-beam antenna as the pivotal reflectometric sensor is systematically designed and realized particularly for enhancement of detection and imaging of subsurface defects in dielectric structures. The antenna is equipped with a compact asymmetric waveguide orthomode transducer, with Teflon used as the internal filling material, in an effort to reduce its size and narrow the beamwidth. The internal dimensions of the realized dual-polarized narrow-beam antenna are optimized via numerical simulations. Based on the optimal design parameters, the antenna is fabricated and assessed through experiments. The experimental results reveal that the fabricated antenna has better metric indicators in terms of a return loss better than 10 dB, isolation better than 40 dB in 30.0 GHz~36.0 GHz and half-power beamwidths below 36.9° at 36.0 GHz. In order to further affirm the applicability of the fabricated antenna for CrPI, an MNT system is established to perform two-dimensional scanning and imaging of back-surface volumetric defects in polyethylene specimens. Based on the image characteristics of CrPI, a flaw-recovery algorithm is proposed to retrieve the defect opening profile. The averaged contrast-to-noise ratio of the processed CrPI-based image is found to be approximately five times larger than that of the raw CrPI-based image and fourteen times bigger than that of the raw CoPI-based image. Experimental results have further indicated that the fabricated antenna is feasible for not only co-polarized inspection (CoPI) but for CrPI, which exhibits higher testing sensitivity and defect-image contrast than CoPI. In conjunction with the flaw-recovery algorithm, by utilizing the dual-polarized narrow-beam antenna with the better metric indicators for CrPI, the image quality of the back-surface flaws in polyethylene slabs can be effectively improved.

This review paper explores the fundamental principles, various applications, and recent advancements of Magnetic Flux Leakage (MFL) technique for defect detection within the evolving field of non-destructive testing (NDT) technology. MFL, a well-established technique, utilizes the interaction between magnetic fields and the material under inspection, offering invaluable insights into the structural integrity of ferromagnetic materials. The review begins by elucidating the fundamental principles of MFL, particularly on the dynamics of magnetic field behaviour and their interaction with defects or anomalies within a material. Next, a brief exploration of the factors influencing MFL signals creates the basis for understanding its robustness and challenges in detecting and characterizing structural defects. This paper then navigates through numerous applications where the versatility of MFL has proven its efficacy. Lastly, the review extends to the latest advancements in MFL inspection techniques, including the incorporation of artificial intelligence and integration with other NDT technologies, showcasing the opportunities MFL poses across various industrial scenarios. In conclusion, this paper seeks to contribute to the ongoing dialogue on the role of MFL in ensuring the structural integrity and safety of critical assets in various industrial sectors.

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.

This research proposes a nondestructive single-sensor acoustic emission (AE) scheme for the detection and localization of cracks in steel rail under loads. In the operation, AE signals were captured by the AE sensor and converted into digital signal data by AE data acquisition module. The digital data were denoised to remove ambient and wheel/rail contact noises, and the denoised data were processed and classified to localize cracks in the steel rail using a deep learning algorithmic model. The AE signals of pencil lead break at the head, web, and foot of steel rail were used to train and test the algorithmic model. In training and testing the algorithm, the AE signals were divided into two groupings (150 and 300 AE signals) and the classification accuracy compared. The deep learning-based AE scheme was also implemented onsite to detect cracks in the steel rail. The total accuracy (average F1 score) under the first and second groupings were 86.6% and 96.6%, and that of the onsite experiment was 77.33%. The novelty of this research lies in the use of a single AE sensor and AE signal-based deep learning algorithm to efficiently detect and localize cracks in the steel rail, unlike existing AE crack-localization technology that relies on two or more sensors and human interpretation.

Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R 2 was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor.