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  • Open Access

    PROCEEDINGS

    Advancing Ultrasonics-Based Techniques for Non-Destructive Evaluation of Additive Manufactured Composites

    Xudong Yu1,*, Hai Shen1, Jingyuan Lu1, Shangqin Yuan2, Ming Huang3

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.2, pp. 1-2, 2024, DOI:10.32604/icces.2024.011546

    Abstract The continuous advancement of Additive Manufacturing (AM) technologies has revolutionized the production of intricate components and reinforced composites with tailored mechanical properties. However, the variability in AM techniques and processing parameters often leads to discrepancies in fibre volume fraction, porosity, and interfaces in AM composites, resulting in dispersed elastic moduli and mechanical responses, which necessitates robustness non-destructive evaluation (NDE) methods. Additionally, AM introduces new defect morphologies, dimensions, and locations, demanding new and more reliable non-destructive testing (NDT) techniques.
    This research commences by quantifying orientation-dependent mechanical properties of laser-sintered nanocomposites of carbon nanotube (CNT) reinforced polyamine (PA).… More >

  • Open Access

    ARTICLE

    YOLO-VSI: An Improved YOLOv8 Model for Detecting Railway Turnouts Defects in Complex Environments

    Chenghai Yu, Zhilong Lu*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3261-3280, 2024, DOI:10.32604/cmc.2024.056413 - 18 November 2024

    Abstract Railway turnouts often develop defects such as chipping, cracks, and wear during use. If not detected and addressed promptly, these defects can pose significant risks to train operation safety and passenger security. Despite advances in defect detection technologies, research specifically targeting railway turnout defects remains limited. To address this gap, we collected images from railway inspectors and constructed a dataset of railway turnout defects in complex environments. To enhance detection accuracy, we propose an improved YOLOv8 model named YOLO-VSS-SOUP-Inner-CIoU (YOLO-VSI). The model employs a state-space model (SSM) to enhance the C2f module in the YOLOv8… More >

  • Open Access

    ARTICLE

    Research on Defect Detection of Wind Turbine Blades Based on Morphology and Improved Otsu Algorithm Using Infrared Images

    Shuang Kang1, Yinchao He1,2, Wenwen Li1,*, Sen Liu2

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 933-949, 2024, DOI:10.32604/cmc.2024.056614 - 15 October 2024

    Abstract To address the issues of low accuracy and high false positive rate in traditional Otsu algorithm for defect detection on infrared images of wind turbine blades (WTB), this paper proposes a technique that combines morphological image enhancement with an improved Otsu algorithm. First, mathematical morphology’s differential multi-scale white and black top-hat operations are applied to enhance the image. The algorithm employs entropy as the objective function to guide the iteration process of image enhancement, selecting appropriate structural element scales to execute differential multi-scale white and black top-hat transformations, effectively enhancing the detail features of defect… More >

  • Open Access

    ARTICLE

    YOLO-RLC: An Advanced Target-Detection Algorithm for Surface Defects of Printed Circuit Boards Based on YOLOv5

    Yuanyuan Wang1,2,*, Jialong Huang1, Md Sharid Kayes Dipu1, Hu Zhao3, Shangbing Gao1,2, Haiyan Zhang1,2, Pinrong Lv1

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4973-4995, 2024, DOI:10.32604/cmc.2024.055839 - 12 September 2024

    Abstract Printed circuit boards (PCBs) provide stable connections between electronic components. However, defective printed circuit boards may cause the entire equipment system to malfunction, resulting in incalculable losses. Therefore, it is crucial to detect defective printed circuit boards during the generation process. Traditional detection methods have low accuracy in detecting subtle defects in complex background environments. In order to improve the detection accuracy of surface defects on industrial printed circuit boards, this paper proposes a residual large kernel network based on YOLOv5 (You Only Look Once version 5) for PCBs surface defect detection, called YOLO-RLC (You… More >

  • Open Access

    ARTICLE

    Multi-Layer Feature Extraction with Deformable Convolution for Fabric Defect Detection

    Jielin Jiang1,2,3,4,*, Chao Cui1, Xiaolong Xu1,2,3,4, Yan Cui5

    Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 725-744, 2024, DOI:10.32604/iasc.2024.036897 - 06 September 2024

    Abstract In the textile industry, the presence of defects on the surface of fabric is an essential factor in determining fabric quality. Therefore, identifying fabric defects forms a crucial part of the fabric production process. Traditional fabric defect detection algorithms can only detect specific materials and specific fabric defect types; in addition, their detection efficiency is low, and their detection results are relatively poor. Deep learning-based methods have many advantages in the field of fabric defect detection, however, such methods are less effective in identifying multi-scale fabric defects and defects with complex shapes. Therefore, we propose… More >

  • Open Access

    ARTICLE

    A Hybrid Deep Learning and Machine Learning-Based Approach to Classify Defects in Hot Rolled Steel Strips for Smart Manufacturing

    Tajmal Hussain, Jungpyo Hong*, Jongwon Seok*

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2099-2119, 2024, DOI:10.32604/cmc.2024.050884 - 15 August 2024

    Abstract Smart manufacturing is a process that optimizes factory performance and production quality by utilizing various technologies including the Internet of Things (IoT) and artificial intelligence (AI). Quality control is an important part of today’s smart manufacturing process, effectively reducing costs and enhancing operational efficiency. As technology in the industry becomes more advanced, identifying and classifying defects has become an essential element in ensuring the quality of products during the manufacturing process. In this study, we introduce a CNN model for classifying defects on hot-rolled steel strip surfaces using hybrid deep learning techniques, incorporating a global… More >

  • Open Access

    ARTICLE

    Surface Defect Detection and Evaluation Method of Large Wind Turbine Blades Based on an Improved Deeplabv3+ Deep Learning Model

    Wanrun Li1,2,3,*, Wenhai Zhao1, Tongtong Wang1, Yongfeng Du1,2,3

    Structural Durability & Health Monitoring, Vol.18, No.5, pp. 553-575, 2024, DOI:10.32604/sdhm.2024.050751 - 19 July 2024

    Abstract The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage, impacting the aerodynamic performance of the blades. To address the challenge of detecting and quantifying surface defects on wind turbine blades, a blade surface defect detection and quantification method based on an improved Deeplabv3+ deep learning model is proposed. Firstly, an improved method for wind turbine blade surface defect detection, utilizing Mobilenetv2 as the backbone feature extraction network, is proposed based on an original Deeplabv3+ deep learning model to address the issue of limited robustness. Secondly, through integrating the concept of… More > Graphic Abstract

    Surface Defect Detection and Evaluation Method of Large Wind Turbine Blades Based on an Improved Deeplabv3+ Deep Learning Model

  • Open Access

    ARTICLE

    A Simple and Effective Surface Defect Detection Method of Power Line Insulators for Difficult Small Objects

    Xiao Lu1,*, Chengling Jiang1, Zhoujun Ma1, Haitao Li2, Yuexin Liu2

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 373-390, 2024, DOI:10.32604/cmc.2024.047469 - 25 April 2024

    Abstract Insulator defect detection plays a vital role in maintaining the secure operation of power systems. To address the issues of the difficulty of detecting small objects and missing objects due to the small scale, variable scale, and fuzzy edge morphology of insulator defects, we construct an insulator dataset with 1600 samples containing flashovers and breakages. Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed. Firstly, a high-resolution feature map is introduced and a small object prediction layer is added so that the model can detect… More >

  • Open Access

    ARTICLE

    SAM Era: Can It Segment Any Industrial Surface Defects?

    Kechen Song1,2,*, Wenqi Cui2, Han Yu1, Xingjie Li1, Yunhui Yan2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3953-3969, 2024, DOI:10.32604/cmc.2024.048451 - 26 March 2024

    Abstract Segment Anything Model (SAM) is a cutting-edge model that has shown impressive performance in general object segmentation. The birth of the segment anything is a groundbreaking step towards creating a universal intelligent model. Due to its superior performance in general object segmentation, it quickly gained attention and interest. This makes SAM particularly attractive in industrial surface defect segmentation, especially for complex industrial scenes with limited training data. However, its segmentation ability for specific industrial scenes remains unknown. Therefore, in this work, we select three representative and complex industrial surface defect detection scenarios, namely strip steel More >

  • Open Access

    ARTICLE

    A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection

    Lanyao Zhang1, Shichao Kan2, Yigang Cen3, Xiaoling Chen1, Linna Zhang1,*, Yansen Huang4,5

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1631-1648, 2024, DOI:10.32604/cmc.2024.046924 - 27 February 2024

    Abstract Unsupervised methods based on density representation have shown their abilities in anomaly detection, but detection performance still needs to be improved. Specifically, approaches using normalizing flows can accurately evaluate sample distributions, mapping normal features to the normal distribution and anomalous features outside it. Consequently, this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network (NF-BMR). It utilizes pre-trained Convolutional Neural Networks (CNN) and normalizing flows to construct discriminative source and target domain feature spaces. Additionally, to better learn feature information in both domain spaces, we propose the Bidirectional Mapping Residual Network (BMR), which maps sample… More > Graphic Abstract

    A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection

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