用3D扫描与深度学习监测电缆绝缘层表面缺陷，提升质量控制精度
Topography scanning as a part of process monitoring in power cable insulation process
- 通过高精度3D扫描与嵌入式计算构建绝缘芯表面全景图
- 发现熔融均匀性是导致截面和长度方向误差的主要因素
- 基于卷积神经网络实现实时表面缺陷检测，适合工业质检场景
我们提出一种新型拓扑扫描系统，用于交联聚乙烯（XLPE）电缆芯的工艺监控。结合现代测量技术与嵌入式高性能计算，实现了对绝缘芯表面的完整、详细的三维映射。研究了截面及纵向几何误差，并识别出熔融均匀性是导致这些误差的关键因素。同时开发了基于深度学习的表面缺陷检测系统，结果表明卷积神经网络适用于实时分析表面测量数据，可实现可靠的表面缺陷检测。
原文摘要 · Abstract (English)
We present a novel topography scanning system developed to XLPE cable core monitoring. Modern measurement technology is utilized together with embedded high-performance computing to build a complete and detailed 3D surface map of the insulated core. Cross sectional and lengthwise geometry errors are studied, and melt homogeneity is identified as one major factor for these errors. A surface defect detection system has been developed utilizing deep learning methods. Our results show that convolutional neural networks are well suited for real time analysis of surface measurement data enabling reliable detection of surface defects.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。