Local Defects Research Articles

Dynamic behavior and spatio-temporal mechanical characteristics of the individual cavitation bubble on hydraulic concrete wall

This study endeavors to delve into the dynamics and consequent mechanical responses induced by an individual cavitation bubble proximal to a concrete surface. Employing the laser-induced cavitation bubbles experimental method, the dynamic behavior of a cavitation bubble near horizontal smooth and locally defective concrete surfaces is systematically investigated. Using computational fluid dynamics (CFD) simulations, the impulsive pressure induced by bubble collapse within a specific surface range is quantitatively investigated. Our results reveal that the presence of local defects on the concrete surface activates gas nuclei within the defects upon bubble shock wave emission. Gas nuclei near the surface center penetrate the bubble, prolonging the bubble’s pulsation period and reducing microjet velocities. During bubble collapse, significant concentrated stresses due to microjets near the surface center may trigger instantaneous brittle failure of the concrete surface. Moreover, shock waves form high-pressure zones within a certain range of the surface. When the dimensionless distance γ ranges from 1.1 to 1.2, a superposition phenomenon of microjets and shock waves is observed, compared to individual effects, enhancing the impulse at central point below the bubble, prolonging the duration of pressure within the specific surface horizontal range, but simultaneously accelerating the pressure’s horizontal attenuation rate along the surface. Compared to the center pressure of the wall surface, the impulse of average pressure better reflects the surface’s pressure response.

D2-SPDM: Faster R-CNN-Based Defect Detection and Surface Pixel Defect Mapping with Label Enhancement in Steel Manufacturing Processes

The steel manufacturing process is inherently continuous, meaning that if defects are not effectively detected in the initial stages, they may propagate through subsequent stages, resulting in high costs for corrections in the final product. Therefore, detecting surface defects and obtaining segmentation information is critical in the steel manufacturing industry to ensure product quality and enhance production efficiency. Specifically, segmentation information is essential for accurately understanding the shape and extent of defects, providing the necessary details for subsequent processes to address these defects. However, the time-consuming and costly process of generating segmentation annotations poses a significant barrier to practical industrial applications. This paper proposes a cost-efficient segmentation labeling framework that combines deep learning-based anomaly detection and label enhancement to address these challenges in the steel manufacturing process. Using ResNet-50, defects are classified, and faster region convolutional neural networks (faster R-CNNs) are employed to identify defect types and generate bounding boxes indicating the defect locations. Subsequently, recursive learning is performed using the GrabCut algorithm and the DeepLabv3+ model based on the generated bounding boxes, significantly reducing annotation costs by generating segmentation labels. The proposed framework effectively detects defects and accurately defines them, even in randomly collected images from the steel manufacturing process, contributing to both quality control and cost reduction. This study presents a novel approach for improving the quality of the steel manufacturing process and is expected to enhance overall efficiency in the steel manufacturing industry.

PDeT: A Progressive Deformable Transformer for Photovoltaic Panel Defect Segmentation

Defects in photovoltaic (PV) panels can significantly reduce the power generation efficiency of the system and may cause localized overheating due to uneven current distribution. Therefore, adopting precise pixel-level defect detection, i.e., defect segmentation, technology is essential to ensuring stable operation. However, for effective defect segmentation, the feature extractor must adaptively determine the appropriate scale or receptive field for accurate defect localization, while the decoder must seamlessly fuse coarse-level semantics with fine-grained features to enhance high-level representations. In this paper, we propose a Progressive Deformable Transformer (PDeT) for defect segmentation in PV cells. This approach effectively learns spatial sampling offsets and refines features progressively through coarse-level semantic attention. Specifically, the network adaptively captures spatial offset positions and computes self-attention, expanding the model’s receptive field and enabling feature extraction across objects of various shapes. Furthermore, we introduce a semantic aggregation module to refine semantic information, converting the fused feature map into a scale space and balancing contextual information. Extensive experiments demonstrate the effectiveness of our method, achieving an mIoU of 88.41% on our solar cell dataset, outperforming other methods. Additionally, to validate the PDeT’s applicability across different domains, we trained and tested it on the MVTec-AD dataset. The experimental results demonstrate that the PDeT exhibits excellent recognition performance in various other scenarios as well.

Experimental Study on the Mechanical Properties of Squat RC Shear Walls with Corrosion Along the Base

In corrosive environments containing chloride and sulfate, the corrosion of steel bars is common along the base of squat RC shear walls (SRCSW) due to problems such as construction quality, concrete stress concentration, local defects, and accumulation of water and corrosive media. In this paper, three SRCSWs are designed and constructed and their mechanical properties assessed. One side of each SRCSW was exposed to a corrosive environment for 70 days, while the other side was subject to the same conditions over different corrosion times (i.e., 0 day, 42 days, and 70 days). Then, the corrosion-induced cracking process, the mechanical properties of SRCSWs corroded along the base, the relationship between the mass loss of total steel bars (MLTSB) in the corroded area and the wall mechanical properties, and the relationship between the average width of corrosion-induced cracks (CICs) and the wall mechanical properties were studied through an accelerated corrosion test and a loading failure test. The results indicate that the area of corrosion-induced cracking on SRCSWs increased with the corrosion time, and the cracking area on the different SRCSWs was approximately identical when the SRCSWs were exposed to the same corrosion time. When the degree of corrosion was different, the loading failure characteristics of the SRCSWs were obviously different, but the failure mode always corresponded to shear failure. The load–displacement curves of the SRCSWs with different degrees of corrosion along the base basically coincided and were linear when the loading was in the elastic stage. Compared to SW-1, the peak load of SW-2 decreased by 4.0%, but that of SW-3 increased by 2.7%. Compared to SW-1, the yield loads of SW-2 and SW-3 decreased by 22.4% and 11.8%, respectively. When the MLTSB increased from 13.05% to 16.71%, the crack, yield, and peak loads of the SRCSWs corroded along the base decreased by 8.8%, 22.4%, and 6.8%, respectively. The cracking, yield, and peak loads of the SRCSWs corroded along the base decreased linearly with the increase in MLTSB and the average width of the CICs, and the corresponding fitting relations were established. The results of this study can serve as a reference for the durability design of SRCSWs in corrosive environments.

Effect of Deposition Temperature on Zn Interstitials and Oxygen Vacancies in RF-Sputtered ZnO Thin Films and Thin Film-Transistors

ZnO thin films were deposited using RF sputtering by varying the argon:oxygen gas flow rates and substrate temperatures. Structural, optical and electrical characterization of ZnO thin films were systematically carried out using X-Ray diffraction (XRD), scanning electron microscopy (SEM), UV–visible spectroscopy, X-Ray photoelectron spectroscopy (XPS) and Hall measurements. Film deposited at room temperature and annealed at 300 °C exhibited low O2 incorporation with localized defects and a high percentage of Zn interstitials. A large crystalline size and fewer grain boundaries resulted in a high Hall mobility of 46.09 cm2/V-s Deposition at higher substrate temperatures resulted in improvement in O2 incorporation through the annihilation of localized defects and decrease in oxygen vacancies and Zn interstitials. Urbach tails within the bandgap were identified using the absorption spectrum and compared with the % defects from XPS. Bottom-gate thin-film transistors were subsequently fabricated on a SiO2/p-Si substrate using the combination of RF sputtering, wet etching and photolithography. Variation in the substrate temperature showed performance enhancement in terms of the leakage current, threshold voltage, sub-threshold swing and ION/IOFF ratio. Thin-film transistor (TFT) devices deposited at 300 °C resulted in an O2-rich surface through chemisorption, which led to a reduction in the leakage current of up to 10−12 A and a 10-fold reduction in the sub-threshold swing (SS) from 30 V to 2.8 V. Further TFT optimization was carried out by reducing the ZnO thickness to 50 nm, which resulted in a field-effect mobility of 1.1 cm2/V-s and ION/IOFF ratio of 105.

Road Defect Identification and Location Method Based on an Improved ML-YOLO Algorithm

The conventional method for detecting road defects relies heavily on manual inspections, which are often inefficient and struggle with precise defect localization. This paper introduces a novel approach for identifying and locating road defects based on an enhanced ML-YOLO algorithm. By refining the YOLOv8 object detection framework, we optimize both the traditional convolutional layers and the spatial pyramid pooling network. Additionally, we incorporate the Convolutional Block Attention to effectively capture channel and spatial features, along with the Selective Kernel Networks that dynamically adapt to feature extraction across varying scales. An optimized target localization algorithm is proposed to achieve high-precision identification and accurate positioning of road defects. Experimental results indicate that the detection accuracy of the improved ML-YOLO algorithm reaches 0.841, with a recall rate of 0.745 and an average precision of 0.817. Compared to the baseline YOLOv8 model, there is an increase in accuracy by 0.13, a rise in recall rate by 0.117, and an enhancement in average precision by 0.116. After the high detection accuracy of road defects was confirmed, generalization experiments were carried out on the improved ML-YOLO model in the public data set. The experimental results showed that compared with the original YOLOv8n, the average precision and recall rate of all types of ML-YOLO increased by 0.075, 0.121, and 0.035 respectively, indicating robust generalization capabilities. When applied to real-time road monitoring scenarios, this algorithm facilitates precise detection and localization of defects while significantly mitigating traffic accident risks and extending roadway service life. A high detection accuracy of road defects was achieved.

Classification, Localization and Quantization of Eddy Current Detection Defects in CFRP Based on EDC-YOLO.

The accurate detection and quantification of defects is vital for the effectiveness of the eddy current nondestructive testing (ECNDT) of carbon fiber-reinforced plastic (CFRP) materials. This study investigates the identification and measurement of three common CFRP defects-cracks, delamination, and low-velocity impact damage-by employing the You Only Look Once (YOLO) model and an improved Eddy Current YOLO (EDC-YOLO) model. YOLO's limitations in detecting multi-scale features are addressed through the integration of Transformer-based self-attention mechanisms and deformable convolutional sub-modules, with additional global feature extraction via CBAM. By leveraging the Wise-IoU loss function, the model performance is further enhanced, leading to a 4.4% increase in the mAP50 for defect detection. EDC-YOLO proves to be effective for defect identification and quantification in industrial inspections, providing detailed insights, such as the correlation between the impact damage size and energy levels.

Narrowband Electroluminescence from Color Centers in Hexagonal Boron Nitride.

Defects in wide bandgap materials have emerged as promising candidates for solid-state quantum optical technologies. Electrical excitation of single emitters may lead to scalable on-chip devices and therefore is highly sought after. However, most wide bandgap materials are not amenable to efficient doping, posing challenges for electrical excitation and on-chip integration. Here, we demonstrate narrowband electroluminescence from visible and near-infrared color centers in hexagonal boron nitride (hBN). We harness van der Waals tunnel junctions of graphene-hBN-graphene. Charge carriers are electrically injected into hBN, exciting localized defects that emit nonclassical light, as characterized by the second order correlation measurement. Remarkably, the devices operate at room temperature and produce robust, narrowband emission spanning from visible to the near-infrared. Our work marks an important milestone in vdW materials and their promising attributes for integrated quantum technologies and on-chip photonic circuits.

Atomic-scale visualization of defect-induced localized vibrations in GaN.

Phonon engineering is crucial for thermal management in GaN-based power devices, where phonon-defect interactions limit performance. However, detecting nanoscale phonon transport constrained by III-nitride defects is challenging due to limited spatial resolution. Here, we used advanced scanning transmission electron microscopy and electron energy loss spectroscopy to examine vibrational modes in a prismatic stacking fault in GaN. By comparing experimental results with ab initio calculations, we identified three types of defect-derived modes: localized defect modes, a confined bulk mode, and a fully extended mode. Additionally, the PSF exhibits a smaller phonon energy gap and lower acoustic sound speeds than defect-free GaN, suggesting reduced thermal conductivity. Our study elucidates the vibrational behavior of a GaN defect via advanced characterization methods and highlights properties that may affect thermal behavior.

Time-varying displacement excitation and dynamic modeling of angular contact ball bearing with local defects

Angular contact ball bearings will produce fault damage when working for a long time, thus affecting the normal operation of the system. This paper takes angular contact ball bearings with local defects on the outer ring as the research object, proposes a method to distinguish different local defect profiles, establishes a generalized characterization model of time-varying displacement excitation of local defects in angular contact ball bearings, and studies the evolution process of local defects and their displacement excitation mechanism. On this basis, considering the influence of time-varying displacement caused by bearing defects on dynamic characteristics, based on Hertz contact theory, a fault dynamics model of angular contact ball bearings is established, and the correctness of the established model is verified by experiments. In addition, the analysis results show that rectangular local defects will eventually evolve into trapezoidal shapes; the displacement excitation change trends induced by different defect morphologies are different; compared with the length of local defects, the width has a greater impact on displacement excitation. The research results provide a theoretical basis for bearing optimization design and fault diagnosis.

Dynamic Modeling of Angular Contact Ball Bearing with Local Defects under EHL Considering Impact Force

In order to analyze the operating status of angular contact ball bearings(ACBBs) with local defects in more detail, a dynamic model of ACBB with local defects considering impact force under elastohydrodynamic lubrication conditions is proposed. Firstly, an elastohydrodynamic lubrication model for defective bearings considering spin is established, the film thickness and film stiffness of the defective bearings are calculated, and then time-varying displacement excitation model and time-varying stiffness model for local defects in angular contact ball bearings is presented. Based on this, an instantaneous impact force function related to defect size and bearing speed is established. Secondly, based on Hertz contact theory and impact force function, a dynamic calculation method for ACBBs with local defects on the outer ring is proposed. Finally, the vibration characteristics of ball bearings with faults are investigated, and the influence of different parameters on the dynamic response of the bearings is analyzed. The calculation results show that under lubrication conditions, the impact force on the vibration of the bearing is significantly weakened. With the increase of defect size and load, the frequency of bearing fault characteristics remain unchanged, but its amplitude increases. As the bearing speed increases, the characteristic frequency and amplitude of bearing faults increase.

The Fusiongram: a periodic weak fault feature extraction strategy and its application in bearing fault diagnosis

Abstract The weak periodic transient impact responses caused by localized defects in rolling bearings are often obscured by complex interferences, such as white noise, random transient impact responses, and periodic responses from system operations. Meanwhile, the fault feature information contributing to damage detection may be distributed across different frequency bands in the vibration signal. Therefore, under the influence of complex interference, it is a challenging problem regarding how to 1) accurately select the frequency bands containing rich fault feature information, 2) effectively extract damage information from different frequency bands, and thereby, 3) successfully achieve fault diagnosis for machinery condition monitoring. To address these issues, this research introduces a novel signal processing strategy, termed as Fusiongram, for extracting weak periodic fault features amidst the influence of complex interferences. Firstly, the method of complementary hierarchical decomposition is proposed, in which the signal is decomposed into multiple components with overlapping frequency contents. Then, an index with interference resistance is constructed to select the components carrying rich damage feature information. Finally, the adaptive threshold denoising and multicomponent normalized averaging techniques are employed to fuse the information from the squared envelope spectra of the selected components, thus obtaining the reconstructed squared envelope spectra for fault diagnosis. The Fusiongram is able to achieve the goal of weak fault feature extraction from signals with complex interference. The analysis results of numerical simulation and experimental testing verify the effectiveness and advantages of the proposed strategy.

Smart structural health monitoring (SHM) system for on-board localization of defects in pipes using torsional ultrasonic guided waves

Most reported research for monitoring health of pipelines using ultrasonic guided waves (GW) typically utilize bulky piezoelectric transducer rings and laboratory-grade ultrasonic non-destructive testing (NDT) equipment. Consequently, the translation of these approaches from laboratory settings to field-deployable systems for real-time structural health monitoring (SHM) becomes challenging. In this work, we present an innovative algorithm for damage identification and localization in pipes, implemented on a compact FPGA-based smart GW-SHM system. The custom-designed board, featuring a Xilinx Artix-7 FPGA and front-end electronics, is capable of actuating the PZT thickness shear mode transducers, data acquisition and recording from PZT sensors and generating a damage index (DI) map for localizing the damage on the structure. The algorithm is a variation of the common source method adapted for cylindrical geometry. The utility of the algorithm is demonstrated for detection and localization of defects such as notch and mass loading on a steel pipe, through extensive finite element (FE) method simulations. Experimental results obtained using a C-clamp for applying mass loading on the pipe show good agreement with the FE simulations. The localization error values for experimental data analysed using C code on a processor implemented on the FPGA are consistent with algorithm results generated on a computer running Python code. The system presented in this study is suitable for a wide range of GW-SHM applications, especially in cost-sensitive scenarios that benefit from on-node signal processing over cloud-based solutions.

3D-Printed Shape Memory and Piezoelectric Bifunctional Thermoplastic Polyurethane/Polyvinylidene Fluoride Porous Composite Scaffold for Bone Regeneration.

Physical stimulations such as mechanical and electric stimulation can continuously work on bone defect locations to maintain and enhance cell activity, and it has become a hotspot for research in the field of bone repair. Herein, bifunctional porous composite scaffolds with shape memory and piezoelectric functions were fabricated using thermoplastic polyurethane (TPU) and poly(vinylidene fluoride) through triply periodic minimal surfaces design and selective laser sintering technology. Thereinto, the shape fixity ratio and recovery ratio of the composite scaffold reached 98.6% and 81.2%, respectively, showing excellent shape memory functions. More importantly, its piezoelectric coefficient (d33 = 2.47 pC/N) is close to the piezoelectric constant of bone tissue (d33 = 0.7-2.3 pC/N), and the voltage released during the compression process can reach 0.5 V. Furthermore, cyclic compression experiments showed that the strength of composite scaffold was up to 8.3 times compared with the TPU scaffold. Besides, the composite scaffold showed excellent cytocompatibility. In conclusion, the composite scaffold is expected to continuously generate mechanical and electric stimulation due to shape memory and piezoelectric function, respectively, which provide an effective strategy for bone repair.

Harmonic flow-field representations of quantum bits and gates

We describe a general procedure for mapping arbitrary n-qubit states to two-dimensional (2D) vector fields. The mappings use complex rational function representations of individual qubits, producing classical vector field configurations that can be interpreted in terms of 2D inviscid fluid flows or electric fields. Elementary qubits are identified with localized defects in 2D harmonic vector fields, and multiqubit states find natural field representations via complex superpositions of vector field products. In particular, separable states appear as highly symmetric flow configurations, making them both dynamically and visually distinct from entangled states. The resulting real-space representations of entangled qubit states enable an intuitive visualization of their transformations under quantum logic operations. We demonstrate this for the quantum Fourier transform and the period finding process underlying Shor's algorithm, along with other quantum algorithms. Due to its generic construction, the mapping procedure suggests the possibility of extending concepts such as entanglement or entanglement entropy to classical continuum systems, and thus may help guide new experimental approaches to information storage and nonstandard computation. Published by the American Physical Society 2024

Autonomous intelligent additive manufacturing of continuous fiber-reinforced composites: data-enhanced knowledgebase and multi-sensor fusion

ABSTRACT Additive manufacturing (AM) are an emerging technique to generate complex structures of composite. However, the instability of the AM process leads to adverse manufacturing effects, including dimensional inaccuracies and poor mechanical performance in CFRP composites. Online monitoring and adaptive control based on autonomous cognition are suggested to ensure the accuracy of as-fabricated parts and enhance their quality upon manufacturing. In this study, a method of online monitoring and self-adaptive control based on multi-sensor fusion is proposed to predict and adjust the process parameters during AM of CFRP. The force sensor, visual camera, and thermal camera are employed to obtain the multiple signals upon manufacturing and then realize the autonomous perception, cognition, and decision features. Herein, the quantitative correlations between local defects and multi-sensing features are established to guide the decision-making of closed-loop adjustment. Besides, an empirical surrogate model between the misalignment of fiber bundles and input parameters is built to predict the proper parameters. Moreover, the temperature difference and contact force are selected as the controlled features, which are acquired using a thermal camera and a force sensor. The proposed system offers a novel framework for bolstering both the stability of the AM process and the quality of fabricated components.

Real-time in-situ coatings corrosion monitoring using machine learning-enhanced triboelectric nanogenerator

Current methods for monitoring coating corrosion are limited by their inability to provide real-time data and dependence on external power sources. This study presents a novel in-situ corrosion monitoring system using a solid-liquid triboelectric nanogenerator (TENG) that converts mechanical energy into electrical signals for self-powered sensing. TENG signals and electrochemical impedance spectra were measured on a dopamine-modified lignin-polydimethylsiloxane coating on steel in 1 M NaCl solution under no corrosion, indentation, pitting, and broken conditions, respectively. We extract time-frequency features from the TENG signals to predict the coating’s corrosion condition by applying a customised convolutional neural network (CNN). By extracting time-frequency features from the TENG signals and applying a custom CNN, a prediction accuracy of 99 % for corrosion classification was achieved. Furthermore, the CNN regression model predicted coating impedance values with a high coefficient of determination (R² = 0.98), demonstrating its effectiveness in tracking corrosion progression. The developed TENG also facilitates defect localisation via a matrix electrode beneath the coating. Our approach introduces a promising real-time technology for in-situ corrosion monitoring.

Study on non-destructive visualization identification of concrete internal cracks based on SH array ultrasound

Ultrasonic nondestructive testing (NDT) technology was one of the most frequently used and fastest-developing NDT techniques in detection field. This method had high detection sensitivity, more accurate defect localization, and was harmless to the human body. With the development of ultrasonic testing technology, modern ultrasonic imaging technology generally had automatic data acquisition and processing functions, which allowed for intuitive and clear recognition of internal defects by the human eyes. The overall purpose of this study was to visualize the cracks in concrete by synthetic aperture focusing technique (SAFT) and full-waveform inversion (FWI) algorithms on the basis of ultrasonic nondestructive testing technology. At present, there have been studies on the use of ultrasonic instruments to detect the internal defects of concrete. However, the accuracy and authenticity of this method have not been compared in previous studies. Therefore, in this experiment, four concrete specimens with different buried depths, angles, shapes, and thicknesses of cracks were designed. Based on SH ultrasonic wave NDT technology, two types of operations were performed on the collected ultrasonic wave data: one was the application of SAFT method, and the other was the first application of FWI method for detecting internal cracks in concrete. The study found that both algorithms were most accurate in determining the burial depth of cracks, with error results not exceeding 10 %. They also showed good recognition effects for the angle, shape, and thickness of the buried cracks. This proved that both identification methods had the capability to recognize internal cracks. The Introview software bundled with the ultrasonic instrument could identify cracks using SAFT directly and quickly. When FWI was used with MATLAB computation, the results were more precise and intuitive. The overall purpose of this study was to detect cracks in concrete by SAFT and FWI algorithms on the basis of ultrasonic nondestructive testing technology. Combining the two crack identification techniques could be of great significance for ensuring the long-term safety and stability of concrete structures, providing strong support for structural maintenance and reinforcement.

Approach Based on the Ordered Fuzzy Decision Making System Dedicated to Supplier Evaluation in Supply Chain Management.

The selection of suppliers represents a pivotal aspect of supply chain management and has a considerable impact on the success and competitiveness of the organization in question. The selection of a suitable supplier is a multi-criteria decision making (MCDM) problem based on a number of qualitative, quantitative, and even conflicting criteria. The aim of this paper is to propose a novel MCDM approach dedicated to the supplier evaluation problem using an ordered fuzzy decision making system. This study uses a fuzzy inference system based on IF-THEN rules with ordered fuzzy numbers (OFNs). The approach employs the concept of OFNs to account for potential uncertainty and subjectivity in the decision making process, and it also takes into account the trends of changes in assessment values and entropy in the final supplier evaluation. This paper's principal contribution is the development of a knowledge base and the demonstration of its application in an ordered fuzzy expert system for multi-criteria supplier evaluation in a dynamic and uncertain environment. The proposed system takes into account the dynamic changes in the value of assessment parameters in the overall supplier assessment, allowing for the differentiation of suppliers based on current and historical data. The utilization of OFNs in a fuzzy model then allows for a reduction in the complexity of the knowledge base in comparison to a classical fuzzy system and makes it more accessible to users, as it requires only basic arithmetic operations in the inference process. This paper presents a comprehensive framework for the assessment of suppliers against a range of criteria, including local hiring, completeness, and defect factors. Furthermore, the potential to integrate sustainability and ESG (environmental, social, and corporate governance) criteria in the assessment process adds value to the decision making framework by adapting to current trends in supply chain management.

Research on Surface Defect Positioning Method of Air Rudder Based on Camera Mapping Model

(1) Background: Air rudders are used to control the flight attitude of aircraft, and their surface quality directly affects flight accuracy and safety. (2) Method: Traditional positioning methods can only obtain defect location information at the image level but cannot determine the defect’s physical surface position on the air rudder, which lacks guidance for subsequent defect repair. We propose a defect physical surface positioning method based on a camera mapping model. (3) Results: Repeated positioning experiments were conducted on three typical surface defects of the air rudder, with a maximum absolute error of 0.53 mm and a maximum uncertainty of 0.26 mm. Through hardware systems and software development, the real-time positioning function for surface defects on the air rudder was realized, with the maximum axial positioning error for real-time defect positioning being 0.38 mm. (4) Conclusions: The proposed defect positioning method meets the required accuracy, providing a basis for surface defect repair in the air rudder manufacturing process. It also offers a new approach for surface defect positioning in similar products, with engineering application value.