Topography scanning as a part of process monitoring in power cable insulation process
This work addresses quality control issues in the power cable manufacturing industry, representing an incremental improvement by applying existing deep learning methods to a specific domain.
The researchers tackled the problem of monitoring geometry errors and surface defects in XLPE cable insulation by developing a topography scanning system with deep learning-based defect detection, achieving reliable real-time detection of surface defects using convolutional neural networks.
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.