Search Results (174)

Coated ferromagnetic conductors (CFCs) are widely used in the engineering field, such as transportation, petrochemicals, energy, etc. Owing to long-term exposure to harsh and corrosive environments, involving large temperature differences, cyclic loading and humidity, hidden corrosion occurring under the coatings of CFCs has been found to be one of the most critical defects posing a severe threat to the structural strength and safety of CFCs. Therefore, it is important to conduct rapid imaging and quantitative evaluation of this hidden corrosion via Non-Destructive Evaluation (NDE) techniques. A magnetic field-viewing film (MFVF) characterizes magnetic fields by displaying corresponding color shifts, offering a direct visual representation of the magnetic field intensity. In light of this, this paper proposes an MFVF-based probe composed of multiple micro-sensor units for fast imaging of hidden corrosion in CFCs. An image-processing technique based on the modified Canny algorithm is subsequently proposed for identification of corrosion opening profiles in MFVF images. Based on the identification results, an assessment of hidden corrosion parameters is conducted. It is inferred from the experimental results that the opening area, depth and volume of hidden corrosion can be quantitatively evaluated, with an average accuracy of 86.1%. Full article

Tensile and compressive stresses generated during the exploitation of wheel rims can lead to significant failures, posing risks to safety and the environment. Among non-destructive evaluation (NDE) methods, ultrasonic velocity measurement has become widely used for assessing stress states in critical rail vehicle components such as wheel rims. In this study, the relationship between ultrasonic wave velocity and applied compressive stresses in aluminum (EN AW-2011) and austenitic stainless steel (1.4301) specimens is investigated. The methodology integrates ultrasonic time-of-flight (TOF) measurements with controlled mechanical loading up to the elastic limit. The results show that ultrasonic velocity increases with applied compressive stress, with an average change of approximately 40 m/s between unloaded and maximum loading conditions. The material type was identified as the dominant factor, with velocity differences of up to 800 m/s between aluminum and steel, while the applied load contributed changes of approximately 200 m/s. Statistical analysis using Design of Experiments (DOE) and ANOVA confirmed the significance of all main factors (p < 0.0001). The findings demonstrate the sensitivity of ultrasonic velocity to elastic stress states and provide a quantitative basis for the development of reliable in situ ultrasonic stress monitoring systems in rail applications. Full article

In the Industrial Internet of Things (IIoT), vision-based industrial detection technology is crucial in the production process and can be used in many smart manufacturing applications, such as automated production control and Non-Destructive Evaluation (NDE). To enable timely and accurate decision-making, the network must transmit product status information to the server under stringent requirements of ultra-reliability and low latency. However, traditional pixel-centric industrial image transmission consumes additional bandwidth, and existing deep learning-based semantic communication systems rely on costly manual annotations. To overcome these limitations, this paper proposes a novel object-centric semantic communication framework based on improved slot attention for Multiple-Input Multiple-Output (MIMO) transmission in a 6G smart manufacturing scenario. First, we propose an improved slot attention method based on unsupervised learning for real-world manufacturing image datasets. The proposed method decouples complex industrial images into different object instances, each corresponding to an independent semantic component slot, effectively isolating task-related visual targets from redundant backgrounds. Furthermore, we propose a priority-based semantic transmission strategy. By quantifying the task-relevant importance of each semantic slot and jointly matching MIMO sub-channels, our method optimizes industrial image transmission streams, ensuring the reliable transmission of the important semantic information. Extensive simulation results demonstrate that the proposed framework significantly enhances communication transmission efficiency. Even under constrained bandwidth ratios and a low Signal-to-Noise Ratio (SNR), our framework achieves superior visual reconstruction quality and improves the Peak Signal-to-Noise Ratio (PSNR) by 4.25 dB compared to existing benchmarks. Full article

Carbon Fiber Reinforced Polymers (CFRP) are extensively utilized in high-performance engineering, yet localized structural discontinuities can severely compromise their integrity. This paper aims to achieve high-sensitivity characterization of such anomalies using a proposed acoustic shearography technique based on continuous acoustic excitation. A comprehensive finite element model (FEM) was developed to clarify the mechanical-energy coupling between the acoustic fields and localized surface strain field modulations. By exploiting ultrasonic energy coupling, the localized features of discontinuities were identified through full-field, non-contact optical measurement of localized phase distortions. Key parameters, including shearing amount, excitation frequency, driving voltage, and geometric characteristics of blind flat-bottom holes (BFBH), were systematically investigated. The results demonstrate a high correlation between FEM simulations and experimental observations quantitatively elucidating how defect diameter and hole depth modulate surface strain distributions. The proposed hybrid acoustic optical approach achieves near-instantaneous full field imaging within a millisecond timeframe typically under 200 ms. Additionally, the methodology leverages localized acoustic resonance to significantly boost the signal-to-noise ratio (SNR) resulting in highly quantified phase map contrast. Full article

To enhance the measuring capabilities of modern Non-Destructive Evaluation (NDE) devices, it has become essential to integrate standardized digitization services and industry-compliant functionalities. This perspective paper examines approaches for improving NDE systems by incorporating key Industry 4.0 technologies, specifically digital representations such as the Asset Administration Shell (AAS) and OPC UA (Open Platform Communications Unified Architecture). We discuss requirements for interoperable, semantically rich descriptions of NDE systems, outline how OPC UA information models and AAS submodels can be combined with MQTT-based transport, and illustrate these concepts through representative prototype implementations, including predictive maintenance and chatbot assistant use cases. By leveraging these technologies, NDE devices can be transformed into interoperable, data-rich, and intelligent components within smart industrial ecosystems. Compared with previous studies, this Perspective is the first to systematically bring together the requirements, architectural patterns, and evaluation criteria for digital representations designed specifically for NDE systems. It also provides, in a practical and accessible way, NDE-focused OPC UA and AAS-based architectures that support both predictive maintenance and LLM-assisted operator guidance. The presented implementations are at an early stage and serve as illustrative examples, while systematic quantitative validation is ongoing and is outlined as future work. Full article

Pipe networks assure the transportation of primary commodities such as water, oil, and natural gas. Quantitative and early detection of defects avoids costly consequences. Due to low cost of water, high-profile accidents, and economic downturns, the research and development of nondestructive evaluation (NDE) and structural health monitoring (SHM) technologies for freshwater mains and urban water networks have received less attention with respect to the gas and oil industries. Moreover, the technical challenges associated with the practical deployment of monitoring systems and the fact that most water pipelines are buried underground demand synergistic interaction across several disciplines, which may limit the transition from laboratory to real structures. This paper reviews the most prominent NDE/SHM technologies for freshwater pipes. The challenges that said infrastructures pose, as well as the methodologies that can be translated into SHM approaches, are highlighted. The scope of this review is to provide a holistic view of the physical principles, the success, and the technological challenges associated with the inspection and monitoring of freshwater pipelines. Full article

Steels remain among the most widely used structural and engineering materials in modern infrastructure, energy systems, and industrial facilities. Their long-term reliability depends critically on the early detection of corrosion damage and microstructural degradation. This review surveys recent advances in two complementary families of non-destructive evaluation (NDE) methods: magnetic techniques, including magnetic Barkhausen noise (MBN), magnetic flux leakage (MFL), eddy current testing (ECT), and magnetic hysteresis analysis; and electrochemical methods including electrochemical impedance spectroscopy (EIS), linear polarization resistance (LPR), scanning vibrating electrode technique (SVET), and electrochemical noise (EN). Recent progress in sensor miniaturization, signal processing algorithms, and multi-technique integration is reviewed. Particular attention is given to the sensitivity of these methods to microstructural changes reported in the literature, including carbide dissolution, phase transformations, temper embrittlement, and sensitization in stainless steels, as well as to the conditions under which such sensitivity has been demonstrated. The potential synergy between magnetic and electrochemical monitoring is discussed as a possible pathway toward more robust, condition-based maintenance frameworks. Challenges related to field deployment, environmental interference, calibration, and data interpretation are identified, and future directions—including machine learning-assisted analysis and multi-physics sensor arrays—are outlined. Full article

Conventional borescopes are limited by inadequate mechanical flexibility, poor environmental adaptability and reachability, and heavy reliance on operator expertise during aero-engine inspections, making it difficult to meet the demands for efficient and dependable in situ nondestructive evaluation (NDE). This paper presents a novel telescopic continuum robot mechanism with an ultra-high aspect ratio (63.75:1) and three constant-curvature segments, achieving a synergistic design between the robot’s body structure and the long-stroke linear actuator of its central backbone to realize ultra-high-aspect-ratio configurations. This design improves the robot’s ability to access complex and confined internal spaces within aero-engines, thereby reducing inspection blind spots. Furthermore, a configuration-space control strategy integrating kinematic decoupling and driving tendon tension compensation is proposed. This strategy addresses the issues of multi-segment actuation coupling and tendon slack, ensuring the motion control performance for in situ aero-engine blade inspection. The feasibility of the mechanism design was validated through an experimental simulation platform incorporating both turbine blade and compressor blade scenarios. This work offers a new solution for in situ NDE in aero-engines by synergistically integrating an innovative ultra-high-aspect-ratio telescopic mechanism with a dedicated configuration-space controller that addresses multi-segment coupling and tendon slack. Full article

Reliable process monitoring and quality evaluation for resistance spot welding (RSW) have become more important now than ever. An ultrasonic probe embedded into welding electrodes has enabled the acquisition of data about molten pool formation throughout welding, but automation of high-performance ultrasonic data analyses is still necessary to fully realize a monitoring system. This work proposes a two-stage deep learning (DL) approach for automated ultrasonic data analysis for RSW processing monitoring. The first stage conducts semantic segmentation on ultrasonic M-scan welding process signatures, yielding masks for identified molten pool and stack regions from which weld penetration measurements can be directly extracted, as well as expulsion occurrences throughout welding. From input images and segmentation outputs, the second stage directly estimates resultant weld nugget diameters using an additional neural network. Both stages leveraged architectures based on TransUNet, mixing elements of both convolutional neural networks (CNN) and vision transformers, and the effect of cross-attention for stack-up sheet thickness data fusion was investigated via an ablation study. Additionally, in the diameter estimation stage, the ablation study included alternative feature extraction architectures in the network and investigated the provision of M-scans to the model alongside segmentation masks. In both cases, cross-attention was determined to improve performance, and in the case of diameter estimation, providing M-scans as input was found to be beneficial in general. With cross-attention, the segmentation approach yielded a mean intersection over union (IoU) of 0.942 on molten pool, stack, and expulsion regions in the M-scans with 13.4 ms inference time. With cross-attention, diameter estimates yielded a mean absolute error of 0.432 mm with 4.3 ms inference time, representing a significant improvement over algorithmic approaches based on ultrasonic time of flight. Additionally, the approach attained >90% probability of detection (POD) at 0.830 mm below the acceptable diameter threshold and <10% probability of false alarm (PFA) at 0.828 mm above the threshold. These results demonstrate a novel production-ready application of DL in ultrasonic nondestructive evaluation (NDE) and pave the way for zero-defect RSW manufacturing. Full article

Transducers for ultrasonic nondestructive evaluation of materials in harsh environments are needed to manage safe operations in a number of industrial applications including power generation, propulsion, and material and chemical processing. Bismuth titanate has a reasonably high Curie temperature and transduces electrical energy into elastic waves and vice versa. Herein, a slurry containing bismuth titanate powder is air-sprayed onto stainless steel substrates, functionalized, and characterized in terms of coating thickness, center frequency and bandwidth, and signal-to-noise ratio. Coatings 40– 70 μm thick had a center frequency of approximately 7 MHz and a broad frequency response range of 3–20 MHz. Transducers were thermally aged at 375 °C for seven days to assess their temperature tolerance. Post-aging analysis revealed a resonance frequency increase, thickness reduction, and microstructural changes, accompanied by a decrease in signal amplitude. Despite these changes, the aged transducers remained operational with good signal-to-noise ratio. Thermal cycling experiments showed that the response of pristine transducers is changed by cycling to 250 °C, while thermally aged transducers exhibited stable ultrasonic performance. Additional experiments on transducers pre-conditioned at 400 °C demonstrated improved thermal resilience after thermal aging at 350 °C. These field deployable air-sprayed BIT transducers are promising candidates for high-temperature NDE applications. Full article

Refill Friction Stir Spot Welding (RFSSW) provides significant advantages over competing spot joining technologies, but detecting RFSSW’s often small and subtle defects remains challenging. In this study, kinematic feedback data from a RFSSW machine’s factory-installed sensors was used to successfully predict defect presence with 96% accuracy (F1 = 0.92) and preliminary multi-class defect diagnosis with 84% accuracy (F1 = 0.82). Thirty adverse treatments (e.g., contaminated coupons, worn tools, and incorrect material thickness) were carried out to create 300 potentially defective welds, plus control welds, which were then evaluated using profilometry, computed tomography (CT) scanning, cutting and polishing, and tensile testing. Various machine learning (ML) models were trained and compared on statistical features, with support vector machine (SVM) achieving top performance on final quality prediction (binary), random forest outperforming other models in classifying welds into six diagnosis categories (plus a control category) based on the adverse treatments. Key predictors linking process signals to defect formation were identified, such as minimum spindle torque during the plunge phase. In conclusion a framework is proposed to integrate these models into a manufacturing setting for low-cost, full-coverage evaluation of RFSSWs. Full article

The porosity of additive manufacturing components significantly impacts their mechanical properties, thereby limiting their widespread application in engineering. Current porosity assessment predominantly relies on destructive testing, underscoring the urgent need for accurate in situ non-destructive testing methods. In this paper, we propose a novel deep learning-assisted non-destructive testing method for porosity assessment in additive manufacturing components. Our approach leverages the high sensitivity of ultrasonic coda waves to minute internal material changes, combined with the powerful feature extraction capability of deep learning. Experimental results demonstrate that ultrasonic coda waves are sensitive to porosity variations in additive manufacturing components. Due to the porosity of additive manufacturing components involves multi-dimensional micro-structural features, conventional parameters such as the correlation coefficient and relative velocity change cannot establish an effective mapping relationship, despite their variation with porosity, thus precluding accurate inversion. To address this challenge, we propose a coda–convolutional neural network–multi-head attention mechanism network. Ultrasonic coda waves can fully interact with pores inside additive manufacturing components, and their signals are rich in porosity-related features. The introduction of deep learning significantly enhances the ability to extract such features. The trained network achieves high-precision porosity prediction with an accuracy of 98%. Our proposed approach reveals the complementary integration of ultrasonic coda waves and deep learning methods: the former provides high sensitivity to porosity changes, while the latter addresses the limitations of difficult extraction of relevant features and unclear complex mapping relationships. This collaborative framework establishes a new solution for high-precision non-destructive testing of additive manufacturing components. Full article

Microelectronic packaging is crucial for protecting, powering, and interconnecting semiconductor chips, playing a critical role in the functionality and reliability of electronic devices. With the growth in complexity and miniaturization of these products, the implementation of efficient inspection techniques becomes crucial in preventing failures that may result in device malfunctions. This review paper examines the progress made in utilizing Scanning Acoustic Microscopy (SAM) to assess the structural integrity of microelectronic systems within the broader field of Nondestructive Evaluation/Testing (NDE/T) methods. With an exclusive emphasis on SAM, we point out SAM technological advancements in multi-die stacking, Through Silicon Vias (TSV), and hybrid bonding inspection that improve inspection sensitivity and resolution required to be prepared for upcoming challenges accompanying 3D- and heterogeneous integration architectures. Some of these approaches compromise the depth of inspection for the benefit of lateral resolution, while others do not sacrifice the in-depth range of evaluation. These developments are of the utmost importance in addressing the substantial obstacles associated with examining microelectronic packages, facilitating the early detection of potential failures, and enhancing the reliability and robustness of semiconductor devices. Furthermore, our discussion consists of the fundamental principles and practical approaches of SAM. It also examines recent investigations that integrate SAM with machine learning concepts and the application of deep learning models in order to automate defect detection and characterization, thus substantially augmenting the efficiency of microelectronic package assessments. Full article

The paper addresses a critical gap in early-stage corrosion detection in reinforced concrete, a leading cause of structural failures with significant impacts on humans, the economy, and the environment. It presents the M5 (Magnetic Force-Induced Vibration Evaluation) method, an innovative Structural Health Monitoring (SHM) approach that avoids damping in concrete by using electromagnetic excitation and transferring rebar vibrations through magnetic coupling over the sample. By inducing and analyzing natural vibrations directly in reinforcement, M5 enables sensitive, non-destructive evaluation (NDE) of corrosion before deterioration occurs. The study follows a systematic literature review based on PRISMA standards and utilizes EmbedSLR v1.0 free software. The methodology combines NDE with IoT deployment using Low-Power Wide Area Networks (LPWANs) and advanced machine learning (ARA) to detect frequency changes caused by corrosion, ensuring continuous monitoring. Findings suggest that M5 has the potential to enhance sustainable asset management by extending infrastructure lifespan, optimizing maintenance, and reducing waste. Its practical implications are significant for urban planners and engineers aiming to align infrastructure management with smart city strategies. The originality of this work lies in integrating electromagnetic NDT with IoT and data-driven decision-making, offering new insights at the intersection of engineering and sustainable smart city management. Full article