NDT, Volume 3, Issue 2 (June 2025) – 8 articles

This study intends to understand the effect of annealing behavior on the microstructure and mechanical and magnetic properties of cold-rolled extra-low-carbon steel. Deformed steel samples are annealed at temperature ranges of 200–690 °C followed by air-cooling. As part of this study, Magnetic Hysteresis loop (MHL) and Barkhausen emission (MBE) measurements are carried out for non-destructive evaluation (NDE) of the mechanical properties that are altered during annealing, viz. recovery and recrystallization. At low annealing temperature ranges 200 < T < 550 °C, the recovery causes no substantial variations in microstructure, hardness value from 191–185 HV, and tensile strength 456–452 MPa, while magnetic coercivity decreases from 293–275 A/m for cold-rolled annealed steels. The microstructural changes due to recovery and recrystallization are examined using transmission electron microscopy and orientation imaging microscopy (OIM) through electron backscattered diffraction (EBSD). Recrystallization is found after annealing at T > 550 °C, confirmed by the lowering of the microstructural KAM value from 0.81° to 0.65° and a hardness drop from 190.02 to 98 HV for cold-rolled extra-low-carbon steel. Full article

New methods for real-time materials phase identification based upon focal construct tomography (FCT) have been examined. Such quantitative assessment has significant potential in sectors where in-line analysis is required, including screening within aviation security. As a recent component of work programs developing FCT, its capability for accurate, quantitative analysis has been assessed for the first time. Diffraction signatures from mixed-phase materials were acquired from an energy-dispersive FCT system running under normal operational conditions. A calibration curve was constructed from the spectra and subsequently employed to assess the composition of ‘blind’ samples. The results demonstrated that this approach was able to precisely predict the polymorphic phase composition of samples to ±5 wt%. Conclusions: The potential impact of these findings is significant and will enable applications of FCT beyond those requiring a phase identification to those necessitating quantification, such as counterfeit medicines, pharmaceutical quality assurance, aging of explosives, and cement production. Full article

This article explores the use of ultrasonic nondestructive evaluation for qualification of laser-DED IN718 samples. The main goal of this article is to identify potential ultrasonic parameters which have highest sensitivity to microstructral changes that result from fabrication of DED samples. The ultrasonic qualification parameters were extracted from ultrasonic testing including velocity and attenuation measurement, and C-Scan imaging. These measurements were further used to extract parameters that quantify the anisotropy, microstructural heterogeneity, and grain scattering. Two laser-DED IN718 samples fabricated with slightly different processing parameters were evaluated to observe the influence of the laser power and scan speed on the qualification parameters. The identified qualification parameters were compared for these two samples, along with a hot-rolled sample that was also used as reference. The results suggest that the anisotropy, attenuation, and heterogeneity were highest in the DED samples compared to the reference sample. The identified qualification parameters seem to capture these changes, suggesting they could be potentially used for qualification of AM parts. Full article

Skin diseases represent a major worldwide health hazard affecting millions of people yearly and substantially compromising healthcare systems. Particularly in areas where dermatologists are scarce, standard diagnostic techniques, which mostly rely on visual inspection and clinical experience, are frequently subjective, time-consuming, and prone to mistakes. This investigation undertakes a comparative analysis of four state-of-the-art deep learning architectures, YOLO11, YOLOv8, VGG16, and ResNet50, in the context of skin disease identification. This study evaluates the performance of these models using pivotal metrics, building upon the foundation of the YOLO paradigm, which revolutionized spatial attention and multi-scale representation. A properly selected collection of 900 high-quality dermatological images with nine disease categories was used for investigation. Robustness and generalizability were guaranteed by using data augmentation and hyperparameter adjustment. By varying benchmark models in balancing accuracy and recall while limiting false positives and false negatives, YOLO11 obtained a test accuracy of 80.72%, precision of 88.7%, recall of 86.7%, and an F1 score of 87.0%. The expedition performance of YOLO11 signifies a promising trajectory in the development of highly accurate skin disease detection models. Our analysis not only highlights the strengths and weaknesses of the model but also underscores the rapid development of deep learning techniques in medical imaging. Full article

Weeds cause significant yield and economic losses by competing with crops and increasing production costs. Compounding these challenges are labor shortages, herbicide resistance, and environmental pollution, making weed management increasingly difficult. In response, precision weed control (PWC) technologies, such as robots and unmanned aerial vehicles (UAVs), have emerged as innovative solutions. These tools offer farmers high precision (±1 cm spatial accuracy), enabling efficient and sustainable weed management. Herbicide spraying robots, mechanical weeding robots, and laser-based weeders are deployed on large-scale farms in developed countries. Similarly, UAVs are gaining popularity in many countries, particularly in Asia, for weed monitoring and herbicide application. Despite advancements in robotic and UAV weed control, their large-scale adoption remains limited. The reasons for this slow uptake and the barriers to widespread implementation are not fully understood. To address this knowledge gap, our review analyzes 155 articles and provides a comprehensive understanding of PWC challenges and needed interventions for scaling. This review revealed that AI-driven weed mapping in robots and UAVs struggles with data (quality, diversity, bias) and technical (computation, deployment, cost) barriers. Improved data (collection, processing, synthesis, bias mitigation) and efficient, affordable technology (edge/hybrid computing, lightweight algorithms, centralized computing resources, energy-efficient hardware) are required to improve AI-driven weed mapping adoption. Specifically, robotic weed control adoption is hindered by challenges in weed recognition, navigation complexity, limited battery life, data management (connectivity), fragmented farms, high costs, and limited digital literacy. Scaling requires advancements in weed detection and energy efficiency, development of affordable robots with shared service models, enhanced farmer training, improved rural connectivity, and precise engineering solutions. Similarly, UAV adoption in agriculture faces hurdles such as regulations (permits), limited payload and battery life, weather dependency, spray drift, sensor accuracy, lack of skilled operators, high initial and operational costs, and absence of standardized protocol. Scaling requires financing (subsidies, loans), favorable regulations (streamlined permits, online training), infrastructure development (service providers, hiring centers), technological innovation (interchangeable sensors, multipurpose UAVs), and capacity building (farmer training programs, awareness initiatives). Full article

One of the most sensitive parts of a wind turbine to environmental influences are the rotating blades. Today, there are many technologies available to assess blade condition, but they all need to be developed to become more cost-effective and more sensitive to fault detection. The algorithms and methods of the modal passport discussed in this paper propose a non-destructive technique already used for helicopter blade condition monitoring and diagnostics. This technique requires adaptation to wind turbine blades because they have larger dimensions, other materials and design, and operate under other conditions. To provide this adaptation, computational and experimental data on the modal properties of the blades must be obtained. The first stage of the study is planned to be performed on a scale model on stationary and rotating test rigs. At this stage of the study, algorithms and methods for the formation of a roadmap to develop a modal passport for a series of composite models of a wind turbine blade are considered. The initial stage of modal passport development included FE modeling of the blade model, calculation of modal parameters, fabricating the blades, and preparing the test equipment. Quantitative assessment of modal tests volume made it possible to plan the step-by-step execution of the roadmap for development and experimental application of the modal passport of wind turbine blade models. Full article

Hybrid simulation integrates numerical and experimental techniques to analyze structural responses under static and dynamic loads. It physically tests components that are not fully characterized while modeling the rest of the structure numerically. Over the past two decades, hybrid testing platforms have become increasingly modular and versatile. This paper presents the development of a robust hybrid testing software framework at the National Laboratory for Civil Engineering (LNEC), Portugal, and evaluates the efficiency of its algorithms. The framework features a LabVIEW-based control and interface application that exchanges data with OpenSees via the OpenFresco middleware using a TCP/IP protocol. Designed for slow to real-time hybrid testing, it employs a predictor–corrector algorithm for motion control, enhanced by an adaptive time series (ATS)-based error tracking and delay compensation algorithm. Its modular design facilitates the integration of new simulation tools. The framework was first assessed through simulated hybrid tests, followed by validation via a hybrid test on a two-bay, one-story steel moment-resisting frame, where one exterior column was physically tested. The results emphasized the importance of the accurate system identification of the physical substructure and the precise calibration of the actuator control and delay compensation algorithms. Full article

Nondestructive testing (NDT) has a crucial role in ensuring the reliability and safety of industrial systems. However, traditional methods typically rely on external sensors, which can lead to increased costs and added complexity. The current study examined an alternative approach using variable-frequency drive (VFD) data for real-time fault detection and predictive maintenance. Most VFDs continuously monitor essential parameters such as motor speed, torque, efficiency, and power consumption, facilitating sensorless condition monitoring that helps detect early-stage motor and apparatus faults without additional hardware. To improve diagnostic capabilities, calculated metrics such as apparent power, efficiency, torque, and energy consumption can deliver more profound insights into system performance, assisting in identifying potential failure patterns. A Python-based data acquisition and visualization system was developed and implemented as an example of a potential solution, enabling centralized monitoring, anomaly detection, and historical data analysis. Future advancements in artificial intelligence and machine learning could further refine automated fault detection by utilizing historical VFD data to predict system failures accurately. By integrating VFD-based diagnostics into NDT, industries can develop scalable, cost-effective, intelligent testing and maintenance solutions that improve reliability and asset management in modern systems. Full article