检索结果-内蒙古大学图书馆

defect detection method based on sparse scanning with laser ultrasonics

SCIENTIFIC REPORTS 2025年第1期15卷 1-11页

作者： Wang, Chenwei Han, Rui Zhang, Yihui Wang, Yuzhong Zi, Yanyang Zhao, Jiyuan Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Beijing Informat Sci & Technol Univ Sch Automat Beijing 100192 Peoples R China

The rapid development of metal additive manufacturing (AM) technology has led to its widespread application across various industries, with quality control of AM components being a current focal point of research. To enable on-line defect detection during the metal AM process using laser ultrasonic technology, this paper proposes a defect detection method based on sparse scanning. By employing sparse scanning to collect data, the method significantly improves detection efficiency while accurately characterizing the position and morphology of defects. This study thoroughly investigates the interaction mechanisms between ultrasonic waves and defects. Ellipses are drawn based on the propagation paths of ultrasonic waves and scanning parameters, and the defect edges are characterized by the intersection points of adjacent ellipses and their corresponding tangent points. Five sets of experiments were designed to examine four typical types of defects, and multiple ultrasonic signals were collected using sparse scanning. For defects larger than 1 mm, the experimental results demonstrate that the proposed method can effectively output the position and edge morphology of the defects. Compared to the Synthetic Aperture Focusing Technique (SAFT) method, this method requires only 15.5% of the scanning data, achieves 31.92% of SAFT's computational efficiency, and has a Mean Absolute Error (MAE) of 27%. For internal hole defect with a diameter of 0.4 mm, consistent results with the SAFT method were obtained using 32% of the data. These experiments validate that the proposed defect detection method based on sparse scanning is an efficient method suitable for on-line defect detection in additive manufacturing.

关键词： Sparse scanning Laser ultrasonics defect detection Additive manufacturing On-line detection

来源：评论

学校读者我要写书评

暂无评论

defect detection Method for Large-Curvature and Highly Reflective Surfaces Based on Polarization Imaging and Improved YOLOv11

引用

PHOTONICS 2025年第4期12卷 368-368页

作者： Yu, Zeyu Wang, Dongyun Wu, Hanyang Zhejiang Normal Univ Sch Engn Jinhua 321005 Peoples R China Key Lab Intelligent Operat & Maintenance Technol & Jinhua 321005 Peoples R China

In industrial manufacturing, product quality is of paramount importance, as surface defects not only compromise product appearance but may also lead to functional failures, resulting in substantial economic losses. Detecting defects on complex surfaces remains a significant challenge due to the variability of defect characteristics, interference from specular reflections, and imaging non-uniformity. Traditional computer vision algorithms often fall short in addressing these challenges, particularly for defects on highly reflective curved surfaces such as aircraft engine blades, bearing surfaces, or vacuum flasks. Although various optical imaging techniques and advanced detection algorithms have been explored, existing approaches still face limitations, including high system complexity, elevated costs, and insufficient capability to detect defects with diverse morphologies. To address these limitations, this study proposes an innovative approach that analyzes the propagation of light on complex surfaces and constructs a polarization imaging system to eliminate glare interference. This imaging technique not only effectively suppresses glare but also enhances image uniformity and reduces noise levels. Moreover, to tackle the challenges posed by the diverse morphology of defects and the limited generalization ability of conventional algorithms, this study introduces a novel multi-scale edge information selection module and a Focal Modulation module based on the YOLOv11 architecture. These enhancements significantly improve the model's generalization capability across different defect types. Experimental results show that, compared to state-of-the-art object detection models, the proposed model achieves a 3.9% increase in precision over the best-performing baseline, along with notable improvements in recall, mAP50, and other key performance indicators.

关键词： polarization imaging defect detection YOLOv11 deep learning

来源：评论

学校读者我要写书评

暂无评论

defect detection of Photovoltaic Cells Based on an Improved YOLOv8

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第3期16卷 495-503页

作者： Li, Zhihui Wang, Liqiang Tianjin Univ Technol & Educ Tianjin 300222 Peoples R China Tianjin Engn Res Ctr Fieldbus Control Technol Tianjin 300202 Peoples R China

Currently defect detection in photovoltaic (PV) cells faces challenges such as limited training data, data imbalance, and high background complexity, which can result in both false positives and false negatives during the detection process. To address these challenges, a defect detection network based on an improved YOLOv8 model is proposed. Firstly, to tackle the data imbalance problem, five data augmentation techniques-Mosaic, Mixup, HSV transformation, scale transformation, and flip-are applied to improve the model's generalization ability and reduce the risk of overfitting. Secondly, SPD-Conv is used instead of Conv in the backbone network, enabling the model to better detect small objects and defects in low-resolution images, thereby enhancing its performance and robustness in complex backgrounds. Next, the GAM attention mechanism is applied in the detection head to strengthen global channel interactions, reduce information dispersion, and enhance global dependencies, thereby improving network performance. Lastly, the CIoU loss function in YOLOv8 is replaced with the Focal-EIoU loss function, which accelerates model convergence and improves bbox regression accuracy. Experimental results show that the optimized model achieves a mAP of 86.6% on the augmented EL2021 dataset, representing a 5.1% improvement over the original YOLOv8 model, which has 11.24 x 10<^>6 parameters. The improved algorithm outperforms other widely used methods in photovoltaic cell defect detection.

关键词： Photovoltaic cells defect detection YOLOv8 loss function

来源：评论

学校读者我要写书评

暂无评论

defect detection in EBSM components through selective box fusion of modern object detection

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-17页

作者： Han, Rui Wang, Chenwei Wang, Yuzhong Zhang, Yihui Guo, Wenhua Zi, Yanyang Zhao, Jiyuan Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Beijing Informat Sci & Technol Univ Sch Automat Beijing 100192 Peoples R China

Additive Manufacturing (AM) technology has gained widespread application across various industries due to its capability to directly produce products from computer-aided design models. Among AM techniques, the Electron Beam Selective Melting (EBSM) process has attracted significant attention, particularly in aerospace and automotive industries, owing to its high precision, speed, and excellent material properties. However, various defects, especially internal defects that inevitably arise during the manufacturing process, significantly limit the performance of EBSM parts. In this study, X-ray computed tomography (CT) was utilized to scan EBSM parts, and cross-sectional images were employed to train several state-of-the-art modern object detection models for evaluating their effectiveness in detecting internal defects. Sparse R-CNN demonstrated the best overall performance, while the YOLO series excelled in specific metrics. To further capitalize on the strengths of different detection models, a model ensemble approach, Selective Box Fusion (SBF) is proposed. This approach employs voting and weighted fusion of detection boxes to mitigate errors inherent in individual models. Experimental results show that the SBF ensemble method effectively integrates the advantages of multiple detection models, leading to improvements across various evaluation metrics compared to individual models and other ensemble methods.

关键词： Additive manufacturing Electron beam selective melting Computed tomography defect detection Model ensemble

来源：评论

学校读者我要写书评

暂无评论

defect detection for Drill Pipe Thread Based on Eddy Current Testing

引用

SENSING AND IMAGING 2024年第1期25卷 1-17页

作者： Li, Jiyao Xu, Lijun Yin, Wuliang Zhou, Zhaoming Xie, Yuedong Beihang Univ Sch Instrumentat & Optoelect Engn Beijing Peoples R China Univ Manchester Sch Elect & Elect Engn Manchester England Southwest Petr Univ Sch Mechatron Engn Chengdu Peoples R China

Drill pipe threads are critical components in oil and gas drilling operations, but are susceptible to surface and subsurface defects that can lead to failures and significant economic losses. Eddy current testing (ECT) has emerged as a viable non-destructive testing (NDT) technique for detecting defects, offering advantages in terms of sensitivity and efficiency. This study investigates the application of differential ECT transmitter-receiver (T-R) probes for their performance of reducing the common sources of noise. A finite-element (FE) model was developed to optimize the probe's configuration and sensing parameters. Subsequently, a detection system was constructed, comprising a customized ECT instrument and a designed thread inspection probe with specialized protective layer. A signal processing algorithm, utilizing morphological and energy features of the defect signal, was designed to distinguish defect signal from noise such as tilt variations. Experimental results validated the efficacy of the system and the proposed algorithm, demonstrating their ability to reliably detect defects in drill pipe threads.

关键词： Eddy current testing Drill pipe thread defect detection Finite element simulation

来源：评论

学校读者我要写书评

暂无评论

defect detection and repair algorithm for structures generated by topology optimization based on 3D hierarchical fully convolutional network

引用

Biomimetic Intelligence & Robotics 2024年第2期4卷 24-31页

作者： Zhiyu Wan Hai Lan Sichao Lin Houde Dai School of Advanced Manufacturing Fuzhou UniversityJinjiang 362251China Quanzhou Institute of Equipment Manufacturing Haixi InstitutesChinese Academy of SciencesJinjiang 362216China Fujian Key Laboratory of Special Intelligent Equipment Safety Measurement and Control Fujian Special Equipment Inspection and Research InstituteFuzhou 350008China

Customized 3D-printed structural parts are widely used in surgical *** satisfy the mechanical properties and kinematic functions of these structural parts,a topology optimization technique is adopted to obtain the optimal structural layout while meeting the constraints and ***,topology optimization currently faces some practical challenges that must be addressed,such as ensuring that structures do not have significant defects when converted to additive manufacturing *** address this problem,we designed a 3D hierarchical fully convolutional network(FCN)to predict the precise position of the defective *** on the prediction results,an effective repair strategy is adopted to repair the defective structure.A series of experiments is conducted to demonstrate the effectiveness of our *** to the 2D fully convolutional network and the rule-based detection method,our approach can accurately capture most defect structures and achieve 89.88%precision and 95.59%***,we investigate the impact of different ways to increase the receptive field of our model,as well as the trade-off between different defect-repairing *** results of the experiment demonstrate that the hierarchical structure,which increases the receptive field,can substantially improve the defect detection *** the best of our knowledge,this paper is the first to investigate 3D defect prediction and repair for topology optimization in conjunction with deep learning algorithms,providing practical tools and new perspectives for the subsequent development of topology optimization techniques.

关键词： Topology optimization Additive manufacturing Deep learning 3D semantic segmentation defect detection

来源：评论

学校读者我要写书评

暂无评论

defect detection on solar cells using mathematical morphology and fuzzy logic techniques

引用

JOURNAL OF OPTICS-INDIA 2024年第1期53卷 249-259页

作者： Wei, Junchao Chang, Zaibin Xian Traff Engn Inst Dept Basic Courses Xian 710300 Shanxi Peoples R China

Solar cells or photovoltaic systems have been extensively used to convert renewable solar energy to generate electricity, and the quality of solar cells is crucial in the electricity-generating process. Mechanical defects such as cracks and pinholes affect the quality and productivity of solar cells. Thus, it is necessary to detect these defects and reject the defected ones from solar cells production line. Various inspection methods have been proposed based on contact and non-contact methods. The contact methods are usually destructive due to the contact to product, but non-contact methods implemented low accuracy rate or high hardware installation cost. Therefore, it is needed to develop a robust non-contact solar cell inspection method with low hardware installation cost. In this paper, we proposed a non-contact and nondestructive automated visual inspection system that was able to perform defect detection using image processing and fuzzy logic techniques. The image processing techniques involved thresholding, mathematical morphology, and edge detection operators. In order to assess the proposed system, comprehensive evaluation systems were conducted and presented which was consisted of module and integrated evaluations. For the purpose of identifying and categorizing errors, performance comparisons between the production rule, Mamdani, and Sugeno fuzzy models were done. The results of the experiments revealed that, using the Mamdani fuzzy model, the accuracy rates for identifying individual and group defects were 97.08% and 96%, respectively.

关键词： Solar cells Automated visual inspection system (AVIS) defect detection Mathematical morphology Fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

defect detection and classification on imec iN5 node BEoL test vehicle with multibeam scanning electron microscope

引用

JOURNAL OF MICRO-NANOPATTERNING MATERIALS AND METROLOGY-JM3 2023年第2期22卷

作者： Neumann, Jens Timo Srikantha, Abhilash Huethwohl, Philipp Lee, Keumsil William, B. James Korb, Thomas Foca, Eugen Garbowski, Tomasz Boecker, Daniel Das, Sayantan Halder, Sandip Carl Zeiss SMT GmbH Oberkochen Germany Carl Zeiss SMT Inc Dublin CA USA Carl Zeiss MultiSEM GmbH Oberkochen Germany imec Leuven Belgium

We present an automated application for defect detection and classification from ZEISS multibeam scanning electron microscope (MultiSEM (R)) images, based on machine learning (ML) technology. We acquire MultiSEM images of a semiconductor wafer suited for process window characterization at the imec iN5 logic node and use a dedicated application to train ML models for defect detection and classification. We show the user flow for training and execution, and the resulting capture and nuisance rates. Due to straightforward parallelization, the application is designed for the large amounts of data generated rapidly by the MultiSEM.

关键词： multibeam scanning electron microscope inspection defect detection defect classification machine learning

来源：评论

学校读者我要写书评

暂无评论

defect detection in Metal-Ceramic Substrate Based on Image Processing and Machine Learning

引用

IEEJ JOURNAL OF INDUSTRY APPLICATIONS 2024年第4期13卷 379-388页

作者： Zou, Min Ueda, Yuji Osanai, Hideyo Matsunaga, Kota Sugawara, Tsuyoshi Kageyama, Yoichi Akita Univ Grad Sch Engn Sci Akita 0108502 Japan Akita Univ Fac Engn Sci Akita 0108502 Japan Dowa Power Device Co Ltd Shiojiri 3990711 Japan

Automatic detection of defects in a substrate is important in the manufacturing process. defect detection in metalceramic substrates relies on manual assembly lines, which are laborious and time consuming. This study develops a novel defect detection method that automatically localizes the substrate area without a background and enhances the defect area by combining images acquired using a platform with different illumination patterns. In this study, we use a defect image dataset to train convolutional neural networks (CNNs) for defect discrimination. Seven evaluation metrics, i.e., accuracy, F1-score, area under the curve, and prediction time of all the patch images in the test set, are comprehensively considered to select the optimal parameter configuration. A developed ResNet-50-based model achieves the highest defect discrimination accuracy of 99.8%. Based on extensive experiments and their results, this study provides clear guidance for devising a feature extraction method based on multidirection filter processing and optimization of CNNs for classification tasks. Finally, a framework for defect detection in a metal-ceramic substrate for practical use in the power device industry is developed.

关键词： defect detection feature extraction CNN classification deep learning

来源：评论

学校读者我要写书评

暂无评论

defect detection in Bidirectional Glass Fabric Reinforced Thermoplastics Based on 3-D-THz Imaging

引用

IEEE TRANSACTIONS ON TERAHERTZ SCIENCE AND TECHNOLOGY 2023年第3期13卷 209-220页

作者： Souliman, Aya Kahl, Matthias Stock, Daniel Moeller, Michael Engel, Bernd Bolivar, Peter Haring Univ Siegen Inst High Frequency & Quantum Elect D-57076 Siegen Germany Univ Siegen Comp Vis Grp D-57076 Siegen Germany Univ Siegen Inst Forming Technol D-57076 Siegen Germany

Nonhomogeneous, bidirectional glass fabric reinforced thermoplastic (GFRT) composites are used in the automobile industry to produce economic and environment friendly vehicles. Despite the fact that GFRT materials have superior mechanical characteristics and lower manufacturing cost compared with traditionalmaterials, they are prone to significant deformation and defect emergence during the forming/manufacturing of parts from prefabricated laminates. Some defects, especially hidden ones like delamination and consolidation defects, can significantly reduce the mechanical properties of the final parts. It is essential to use 3-D nondestructive testing technology to detect and identify such hidden defects. This research investigates the ability of 3-DTHz imaging to detect hidden defects (i.e., delamination and consolidation) in GFRT composite materials. Frequency modulated continuous wave (FMCW) technique is used to generate 3-D-THz cross-sectional images. Nondefective and defective samples with a thickness of 1.5 mm have been measured with two 3-D THz imaging systems operating in the frequency range from 238 to 316 GHz and from 499 to 733 GHz. In addition, a simulation of the reflected FMCW spectrum is presented to analyze the measurements and intercompare the performance of the two imaging systems. A systematic analysis in a large sample set demonstrates that a delamination defect with a thickness of 125 mu m as well as a consolidation defect with a difference in thickness less than 0.4 mm can be successfully detected by the 700-GHz imaging system.

关键词： 3-D-THz imaging composite materials defect detection frequency modulated continuous wave (FMCW) glass fabric reinforced thermoplastic (GFRT) nondestructive testing (NDT) terahertz (THz)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：