# Automated Optical Inspection and Image Analysis of Superconducting Radio-Frequency Cavities

## TL;DR

This paper presents an automated optical inspection system and image analysis method for superconducting RF cavities, achieving high accuracy in surface characterization and correlating surface features with cavity performance.

## Contribution

The study introduces a novel image processing code and variables for analyzing cavity surfaces, enabling detailed surface characterization and correlation with performance metrics.

## Key findings

- Surface roughness measurements agree with profilometry.
- Image analysis identifies fabrication properties like welding speed.
- Strong correlation (−0.93) between surface variables and accelerating field.

## Abstract

The inner surface of superconducting cavities plays a crucial role to achieve highest accelerating fields and low losses. For an investigation of this inner surface of more than 100 cavities within the cavity fabrication for the European XFEL and the ILC HiGrade Research Project, an optical inspection robot OBACHT was constructed. To analyze up to 2325 images per cavity, an image processing and analysis code was developed and new variables to describe the cavity surface were obtained. The accuracy of this code is up to 97% and the PPV 99% within the resolution of 15.63 $\mu \mathrm{m}$. The optical obtained surface roughness is in agreement with standard profilometric methods. The image analysis algorithm identified and quantified vendor specific fabrication properties as the electron beam welding speed and the different surface roughness due to the different chemical treatments. In addition, a correlation of $\rho = -0.93$ with a significance of $6\,\sigma$ between an obtained surface variable and the maximal accelerating field was found.