Image Analysis Capabilities and Methodologies of Nb3Sn Rutherford Cables

Abstract

The unique Rutherford cabling facility at the Berkeley Center for Magnet Technology, LBNL, has been leading the production of a range of Nb <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Sn cables for a variety of magnet projects domestically and internationally. Cable fabrication is a critical step in accelerator magnet production: to ensure tight dimensional control of the multi-turn coils, fine tolerances must be kept on the cable width, thickness, keystone angle [1] with minimal degradation to the conductor. In addition to these dimensional measurements, we implemented an in-line image acquisition system that can monitor for critical defects such as cross-overs, as well as track the extent of strand deformation at the cable edges. At LBNL, we have collected a large dataset to establish the association between the cable edge facet dimension and the cross-sectional subelement damages. We thus can use image analysis on the facet as an integral and critical quick turn-around-time quality control monitor. Although such measurements are not direct and require a baseline for a given conductor and cable geometry combination, they are non-destructive. They can be measured across the entire length of the cable, which contrasts with critical current or residual resistance ratio measurements, which are only made at the point and tail of the cable and require a lengthy heat-treatment. Moreover, the high frequency of image acquisition coupled with Fast Fourier Transform analysis can give us insights into the key components of the cabling machine as they tend to produce periodic variations within the cable. Such analysis can help find potential faults and inform maintenance planning. This work describes our setup of in-line imaging of Rutherford cables during manufacture, the subsequent image analysis, and data processing. Several case studies illustrate typical usage of the system and lessons learned.