Scholarlink

<p>Ultrasonic C-scan imaging visually presents the shape and size information of defects in specimens, but its gate parameters selection and the structural and environmental noise in ultrasonic echo signals seriously affect the clarity of defect images. The <i>k</i>-means clustering algorithm can effectively distinguish between defect and background areas in the original image, but still suffers from problems such as the inability of the distance metric to differentiate sample attribute differences, the sensitivity to the choice of initial cluster centers, and the need for manual predefinition of the <i>k</i> value. Hence, a new method of ultrasonic image segmentation is proposed through improving the variational mode decomposition (VMD) method, introducing the autocorrelation algorithm, and modifying the <i>k</i>-means clustering algorithm, aiming to improve the segmentation accuracy of defect and background areas in Carbon Fiber Reinforced Polymer (CFRP) laminates. The VMD method is improved by optimizing the modal decomposition number and penalty factor, and then is used to decompose the ultrasonic echo signal. The Euclidean distance is adopted to distinguish between denoised defect and non-defect signals, achieving automatic high-resolution imaging of defects. The <i>k</i>-means clustering algorithm is modified by using the Mahalanobis distance, <i>k</i>-means +  + algorithm, and elbow method, effectively segmenting defect and background areas. The phased array equipment is employed to conduct ultrasonic testing on CFRP laminates with delamination defects of various shapes, depths, and sizes, and the experimental results are compared with those of the proposed ultrasonic image segmentation detection method. The results show that the improved VMD and autocorrelation algorithm significantly reduces the interference of background noise and improves the resolution in automatic imaging. And, the improved <i>k</i>-means clustering algorithm effectively distinguishes between defect damage and target background areas, and reduces manual parameters dependence during clustering.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An Ultrasonic Image Segmentation Detection Method Based on Automatic High-Resolution Imaging and Modified k-means Clustering Algorithm for CFRP Laminates with Delamination Defects

  • 期刊论文

  • 发表日期：

    2026年5月29日

查看全文
  • Xu Zhang,
  • Yiting Shi,
  • Congxiao Xu,
  • Bo Li,
  • Wei Li

摘要

Ultrasonic C-scan imaging visually presents the shape and size information of defects in specimens, but its gate parameters selection and the structural and environmental noise in ultrasonic echo signals seriously affect the clarity of defect images. The k-means clustering algorithm can effectively distinguish between defect and background areas in the original image, but still suffers from problems such as the inability of the distance metric to differentiate sample attribute differences, the sensitivity to the choice of initial cluster centers, and the need for manual predefinition of the k value. Hence, a new method of ultrasonic image segmentation is proposed through improving the variational mode decomposition (VMD) method, introducing the autocorrelation algorithm, and modifying the k-means clustering algorithm, aiming to improve the segmentation accuracy of defect and background areas in Carbon Fiber Reinforced Polymer (CFRP) laminates. The VMD method is improved by optimizing the modal decomposition number and penalty factor, and then is used to decompose the ultrasonic echo signal. The Euclidean distance is adopted to distinguish between denoised defect and non-defect signals, achieving automatic high-resolution imaging of defects. The k-means clustering algorithm is modified by using the Mahalanobis distance, k-means +  + algorithm, and elbow method, effectively segmenting defect and background areas. The phased array equipment is employed to conduct ultrasonic testing on CFRP laminates with delamination defects of various shapes, depths, and sizes, and the experimental results are compared with those of the proposed ultrasonic image segmentation detection method. The results show that the improved VMD and autocorrelation algorithm significantly reduces the interference of background noise and improves the resolution in automatic imaging. And, the improved k-means clustering algorithm effectively distinguishes between defect damage and target background areas, and reduces manual parameters dependence during clustering.