Search Results (435)

Nowadays, a single detection method is insufficient for comprehensively and clearly identifying both surface defects and inner defects in titanium alloys. To address this limitation, this paper proposes a titanium alloy defect detection method based on optical–acoustic image fusion. A detection system was developed to achieve comprehensive and precise inspection of titanium alloys by integrating advanced deep learning-based optical testing technology, reliable C-scan ultrasonic detection technology, and information fusion techniques. Furthermore, the PC software can output interactive fusion results and generate decision-level detection reports. The experimental results demonstrate that the surface defect detection algorithm achieves an accuracy of 99.0%, with a surface defect size measurement resolution of 0.01 mm, an internal defect size measurement resolution of 1 mm, and a positional error within 2 mm. It was found that the proposed method provides a potential solution for the practical application of inspecting surface defects and inner defects in the materials. Full article

The use of aerial robots for inspection and maintenance in industrial settings demands high maneuverability, precise control, and reliable measurements. This study explores the development of a fully customized unmanned aerial manipulator (UAM), composed of a tilting drone and an articulated robotic arm, designed to perform non-destructive in-contact inspections of iron structures. The system is intended to operate in complex and potentially hazardous environments, where autonomous execution is supported by shared-control strategies that include human supervision. A parallel force–impedance control framework is implemented to enable smooth and repeatable contact between a sensor for ultrasonic testing (UT) and the inspected surface. During interaction, the arm applies a controlled push to create a vacuum seal, allowing accurate thickness measurements. The control strategy is validated through repeated trials in both indoor and outdoor scenarios, demonstrating consistency and robustness. The paper also addresses the mechanical and control integration of the complex robotic system, highlighting the challenges and solutions in achieving a responsive and reliable aerial platform. The combination of semi-autonomous control and human-in-the-loop operation significantly improves the effectiveness of inspection tasks in hard-to-reach environments, enhancing both human safety and task performance. Full article

The integrity of bolted components primarily relies on the quality of interfacial contact, which is achieved by maintaining prescribed bolt torque levels. However, challenges arise from corrosion-induced bolt head damage, potentially compromising the bolt preload, and quantifying such effects remains unanswered. Many studies often compare bolt corrosion’s effects to bolt loosening as both affect the interfacial contact stresses to some extent. This technical study aimed to investigate whether a correlation exists between the impact of bolt head damage and the different levels of bolt torque. Guided wave ultrasonic testing (UT) was implemented for this investigation. Laboratory experiments were conducted to monitor the transmission of ultrasonic signals across the bolted interface first during the bolt-tightening process. Once the highest bolt torque was achieved, the process was repeated for a simplified corrosion scenario, simulated by artificially damaging the bolt head in a controlled manner. The analysis focused on studying the transmission of signal energy for both scenarios. The findings revealed different trends for the signal energy transmission during bolt tightening, which are subjective to the inspection frequency. On the contrary, even at an advanced level of bolt head damage corresponding to 16% mass loss, no clear or monotonic trend was observed in the total transmitted energy. While the total energy remained relatively stable across all inspection frequencies, distinct waveform changes, such as energy redistribution and the emergence of additional wave packets, were observed. The findings emphasize the need for more advanced waveform-based analysis techniques to detect and interpret subtle changes caused by bolt degradation. Full article

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

Acoustic tomography is a non-destructive technique used in the internal assessment of standing trees. Various researchers have focused on developing analytical tools using this technique, demonstrating that they can detect internal biodeterioration in cross-sections with good accuracy. This study evaluates the use of two ultrasonic wave devices with different frequencies (USLab and Sylvatest Duo) and a stress wave device (Microsecond Timer) to generate acoustic tomography using ImageWood VC1 software. The tests were carried out on 12 cross-sections of urban trees in the city of Madrid of the species Robinia pseudoacacia L., Platanus × hybrida Brot., Ulmus pumila L., and Populus alba L. Velocity measurements were made, forming a diffraction mesh in both standing trees and logs after cutting them down. An inspection was carried out with a perforation resistance drill (IML RESI F-400S) in the radial direction in each section, which allowed for more precise identification of defects and differentiating between holes and cracks. The various defects were determined with greater accuracy in the tomographic images taken with the higher-frequency equipment (45 kHz), and the combination of ultrasonic tomography and the use of the inspection drill can provide a more accurate representation of the defects. Full article

Structural health monitoring (SHM) plays a pivotal role in ensuring the integrity and safety of critical infrastructure and mechanical components. While traditional non-destructive testing (NDT) methods offer high-resolution data, they typically require periodic access and disassembly of equipment to conduct inspections. In contrast, SHM employs permanently installed, cost-effective sensors to enable continuous monitoring, though often with reduced detail. This study presents an integrated hybrid SHM-NDT methodology enhanced by deep learning to enable the real-time monitoring and classification of mechanical stresses in structural components. As a case study, a 6-foot-long parallel flange I-beam, representing bridge truss elements, was subjected to variable bending loads to simulate operational conditions. The hybrid system utilized an ultrasonic transducer (NDT) for excitation and piezoelectric sensors (SHM) for signal acquisition. Signal data were analyzed using 1D and 2D convolutional neural networks (CNNs), long short-term memory (LSTM) models, and random forest classifiers to detect and classify load magnitudes. The AI-enhanced approach achieved 100% accuracy in 47 out of 48 tests and 94% in the remaining tests. These results demonstrate that the hybrid SHM-NDT framework, combined with machine learning, offers a powerful and adaptable solution for continuous monitoring and precise damage assessment of structural systems, significantly advancing maintenance practices and safety assurance. Full article

With the rapid expansion of railway networks and increasing operational complexity, intelligent rail damage detection has become crucial for ensuring safety and improving maintenance efficiency. Traditional physical inspection methods (e.g., ultrasonic testing, magnetic flux leakage) are limited in terms of efficiency and environmental adaptability. This study proposes a machine vision-based approach leveraging deep learning to identify four primary types of rail damages: corrugations, spalls, cracks, and scratches. A self-developed acquisition device collected 298 field images from the Chongqing Metro system, which were expanded into 1556 samples through data augmentation techniques (including rotation, translation, shearing, and mirroring). This study systematically evaluated three object detection models—YOLOv8, SSD, and Faster R-CNN—in terms of detection accuracy (mAP), missed detection rate (mAR), and training efficiency. The results indicate that YOLOv8 outperformed the other models, achieving an mAP of 0.79, an mAR of 0.69, and a shortest training time of 0.28 h. To further enhance performance, this study integrated the Multi-Head Self-Attention (MHSA) module into YOLO, creating MHSA-YOLOv8. The optimized model achieved a significant improvement in mAP by 10% (to 0.89), increased mAR by 20%, and reduced training time by 50% (to 0.14 h). These findings demonstrate the effectiveness of MHSA-YOLO for accurate and efficient rail damage detection in complex environments, offering a robust solution for intelligent railway maintenance. Full article

Purpose: The aim of this study was to compare the effect of rotary (air-driven, electric-driven) and oscillating (piezoelectric ultrasonic) handpieces on the quality of crown preparation, marginal integrity, and internal adaptation of monolithic zirconia crowns. Materials and Methods: Seventy-two standardized premolar preparations were performed using the air-driven handpiece with a guide pin-ended tapered fissure diamond bur on a modified dental surveyor. The finishing process utilized three handpiece types (n = 24/group) with fine/superfine diamond burs under controlled force with a fixed number of rotations and controlled advancement time. Marginal/internal adaptation was evaluated via the triple-scan technique; defects (marginal, axial, and occlusal) were quantified based on predefined criteria through the inspection of the Standard Tessellation Language (STL) file. Results: One-way ANOVA with Tukey HSD and Kruskal–Wallis with Dunn–Bonferroni tests were utilized. The marginal gap showed no significant differences (p > 0.05, η² = 0.04). The electric handpiece outperformed the ultrasonic (p = 0.023, η² = 0.105) in internal adaptation, while the air-driven showed no differences (p > 0.05). The ultrasonic handpiece produced fewer marginal defects than the air-driven (p = 0.039, ε² = 0.132), but more axial defects (median 9 vs. 6, p = 0.014, ε² = 0.168) than the electric handpiece and occlusal defects (5 vs. 3, 4 p = 0.007, p = 0.015, ε² = 0.227) than rotary handpieces. The air-driven handpiece exhibited comparable defect numbers to the electric handpiece without statistical significance (p > 0.05). Conclusions: Handpiece selection had a small effect on marginal adaptation but more pronounced effects on overall defect formations and internal adaptation. The ultrasonic handpiece’s decreased marginal defects but variable axial/occlusal results reveal technological constraints, whereas rotary handpieces’ consistency reflects their operator-dependent nature. Full article

Laser marking on wafers can introduce various defects such as inconsistent mark quality; under- or over-etching, and misalignment. Excessive laser power and inadequate cooling can cause burning or warping. These defects were inspected using machine vision, confocal microscopy, optical and scanning electron microscopy, acoustic/ultrasonic methods, and inline monitoring and coaxial vision. Machine learning has been successfully applied to improve the classification accuracy, and we propose a random forest algorithm with a training database to not only detect the defect but also trace its cause. Four causes have been identified as follows: unstable laser power, a dirty laser head, platform shaking, and voltage fluctuation of the electrical power. The object-matching technique ensures that a visible image can be utilized without a precise location. All inspected images were compared to the standard (qualified) product image pixel-by-pixel, and then the 2D matrix pattern for each type of defect was gathered. There were 10 photos for each type of defect included in the training to build the model with various labels, and the synthetic testing images altered by the defect cause model for laser marking defect inspection had accuracies of 97.0% and 91.6% in sorting the error cause, respectively Full article

The inspection of oil storage tanks is a critical measure to prevent the risk of oil leakage. Therefore, research has focused on magnet-type climbing robots for automated tank inspections. While existing magnet-type climbing robots have demonstrated significant improvements in climbing steel structures, their capability in terms of metal thickness measurement has not been previously evaluated. During thickness inspections, ultrasonic thickness sensors require a probe to be pressed against target surfaces. To automate metal thickness measurements, this pressing motion of the probe needs to be performed by the robot. This study introduces a novel metal thickness measurement device comprising an ultrasonic probe, a linear actuator, a gel pump, and a pressure sensor designed for a magnet-type climbing robot. The linear actuator moves the probe to its initial position, the gel pump injects a coupling gel, and then the actuator moves the probe to the surface and back. Finally, our prototype of an ultrasonic probe with a linear actuator was installed on a magnet-type climbing robot to demonstrate its functionality in a practical application regarding an oil storage tank inspection system. The prototype achieved a measurement success rate of 65.9% and an average error of 0.7% compared to a reference thickness. This article details the design and development of the ultrasonic probe with a linear actuator to enable the probe to make contact with the surface. It then details the experimental results and evaluation of metal thickness measurement performed using the prototype and the climbing robot. Full article

Trees play a vital role in urban environments by mitigating heat islands, floods, and pollution, while promoting public health and well-being. Acoustic tomography is an effective tool for assessing tree integrity, but its high-cost limits widespread use. To reduce costs, this study evaluated the use of ultrasonic tomography with standardized velocity thresholds (VTs) for detecting cavities and decay in trunks. A total of 38 discs from 21 trees species were analyzed using different VTs (35%, 40%, 45%, and 50%). The results showed that thresholds of 35% Vmax for cavity detection and 50% Vmax for cavity with decay detection can be adopted for tomographic image assessments of trees, regardless of species. Using the same velocity thresholds regardless of species enables the practical application of the technology, with average accuracy losses (below 5%) that are quite reasonable considering the variability of the material under inspection. These findings support the broader use of technology in tree failure risk assessments. Full article

Non-destructive testing (NDT) and non-destructive evaluation (NDE) are essential tools for ensuring the structural integrity, safety, and reliability of critical systems across the aerospace, civil infrastructure, energy, and advanced manufacturing sectors. As engineered materials evolve into increasingly complex architectures such as fiber-reinforced polymers, fiber–metal laminates, sandwich composites, and functionally graded materials, traditional NDT techniques face growing limitations in sensitivity, adaptability, and diagnostic reliability. This comprehensive review presents a multi-dimensional classification of NDT/NDE methods, structured by physical principles, functional objectives, and application domains. Special attention is given to hybrid and multi-material systems, which exhibit anisotropic behavior, interfacial complexity, and heterogeneous defect mechanisms that challenge conventional inspection. Alongside established techniques like ultrasonic testing, radiography, infrared thermography, and acoustic emission, the review explores emerging modalities such as capacitive sensing, electromechanical impedance, and AI-enhanced platforms that are driving the future of intelligent diagnostics. By synthesizing insights from the recent literature, the paper evaluates comparative performance metrics (e.g., sensitivity, resolution, adaptability); highlights integration strategies for embedded monitoring and multimodal sensing systems; and addresses challenges related to environmental sensitivity, data interpretation, and standardization. The transformative role of NDE 4.0 in enabling automated, real-time, and predictive structural assessment is also discussed. This review serves as a valuable reference for researchers and practitioners developing next-generation NDT/NDE solutions for hybrid and high-performance structures. Full article

Rising raw material costs and complex global supply chains have reduced the durability and availability of conveyor belts. In response, condition-based maintenance (CBM) with in situ diagnostics has become essential. This case study from a Polish lignite mine shows how subjective visual inspections were replaced with objective, repeatable measurements of belt core condition and thickness. Shifting refurbishment decisions from the plant to the conveyor improved success rates from 70% to over 90% and optimized belt lifecycle management. Sensor-based monitoring enables predictive maintenance, reduces premature or delayed replacements, increases belt reuse, lowers costs, and supports the circular economy by extending belt core life and reducing raw material demand. The study demonstrates how real-time, sensor-based diagnostics using inductive and ultrasonic technologies supports predictive maintenance of conveyor belts, improving refurbishment efficiency and lifecycle management. Full article

In recent years, the building insulation layer peeling caused by quality problems has brought about safety hazards to human life. Existing means of non-destructive testing of building insulation layers, including laser scanning, infrared thermal imaging, ultrasonic testing, acoustic emission, ground-penetrating radar, etc., are unable to simultaneously guarantee the detection depth and resolution of the insulation layer defects, not to mention high-precision imaging of the insulation layer structure. A new type of high-precision imaging radar is specifically designed for the quantitative quality inspection of external building insulation layers in this paper. The center frequency of the radar is 8800 MHz and the −10 dB bandwidth is 3100 MHz, which means it can penetrate the insulated panel not less than 48.4 mm thick and catch the reflected wave from the upper surface of the bonding mortar. When the bonding mortar is 120 mm away from the radar, the radar can achieve a lateral resolution of about 45 mm (capable of distinguishing two parties of bonding mortar with a 45 mm gap). Furthermore, an ultra-wideband high-bunching antenna is designed in this paper combining the lens and the sinusoidal antenna, taking into account the advantages of high directivity and ultra-wideband. Finally, the high-precision imaging of data collected from multiple survey lines can visually reveal the distribution of bonded mortar and the bonding area. This helps determine whether the bonding area meets construction standards and provides data support for evaluating the quality of the insulation layer. Full article

Single-crystal superalloys have attracted considerable interest in aero engine blade manufacture due to their superior mechanical properties, which maintain structural integrity at high temperatures. However, their anisotropic microstructure results in direction-dependent properties that pose a challenge for defect detection. This study proposes a methodology to determine the crystal orientation, which is subsequently used to improve the Total Focusing Method (TFM) by incorporating the refracted beam directivity. Firstly, simulations were performed using semi-analytical models (CIVA software 2023 SP4.1) to reproduce different grain orientations. The results were then post-processed to determine the grain orientation. Finally, the TFM was adapted to take into account not only the velocity variations due to orientation but also the directivity of the ultrasonic beam based only on slowness curves. The implementation of this methodology has improved the defect detection capability, optimizing the defect positioning by up to 61% and increasing the signal-to-noise ratio by up to 5 dB. This study demonstrates the effectiveness of an adapted inspection procedure for single crystals. Full article