Machine learning applied to X-ray tomography as a new tool to analyze the voids in RRP Nb3Sn wires

Abstract

The electro-mechanical and electro-thermal properties of high-performance Restacked-Rod-Process (RRP) Nb3Sn wires are key factors in the realization of compact magnets above 15 T for the future particle physics experiments. Combining X-ray micro-tomography with unsupervised machine learning algorithm, we provide a new tool capable to study the internal features of RRP wires and unlock different approaches to enhance their performances. Such tool is ideal to characterize the distribution and morphology of the voids that are generated during the heat treatment necessary to form the Nb3Sn superconducting phase. Two different types of voids can be detected in this type of wires: one inside the copper matrix and the other inside the Nb3Sn sub-elements. The former type can be related to Sn leaking from sub-elements to the copper matrix which leads to poor electro-thermal stability of the whole wire. The second type is detrimental for the electro-mechanical performance of the wires as superconducting wires experience large electromagnetic stresses in high field and high current conditions. We analyze these aspects thoroughly and discuss the potential of the X-ray tomography analysis tool to help modeling and predicting electro-mechanical and electro-thermal behavior of RRP wires and optimize their design.

Tomography analysis tool: an application for image analysis based on unsupervised machine learning

“…Currently there are not known applications readily available to separate the components in the images of a superconducting wire exploiting the k-means algorithm. Therefore, the goodness of the result was evaluated observing the 3D reconstruction of several Nb 3 Sn superconducting wires and analysing their voids distribution [12]. Following the 3D reconstruction, it was possible to evaluate if any component was missing in the analysed images (approximately 500 images for every batch).…”

“…More examples of analyses performed using unsupervised machine learning applied to superconducting wires can be found in [12] [25].…”

“…A fundamental step on this path is the 3D reconstruction of the wire geometry, which is necessary to create a Finite Element Models (FEM) at sub-elements level, that would allow further optimization of electromechanical properties of Nb 3 Sn wires. To reach this goal, we developed a tool to characterize completely the internal structure of Nb 3 Sn wires applying unsupervised machine learning to x-ray micro-tomography [12]. Synchrotron tomography is the perfect technology to study in a non-destructive way the internal features of a dense object, and it was used successfully in the past to study and characterize the voids formation in Internal-Tin Nb 3 Sn wires [13,14].…”

“…The Tomography Analysis Tool (TAT) is a Python application that enables a user-friendly post-process of tomographic images based on k-means, i.e. the same unsupervised machine learning used in [12]. TAT exploited k-means to divide the different components of a wire in separate clusters, based on the brightness and colour of the corresponding pixels, and save them as separate images and matrices compatible with other softwares, e.g., MATLAB, Python or ImageJ.…”

The Application of X-ray Micro-CT in the Study of HTS Tape Coils

“…For this, three samples were produced and scanned using computed tomography (CT) with a Nordson Dage XD7600NT X-ray Inspection System with a μCT module. X-ray micro-CT inspection is considered as nondestructive [12,13], since the CT scan is performed on the entire coil, after which it remains intact and аfunctional. It is known that X-rays could provide a negative influence on the HTS layer in tape [14].…”