Manufacturing process anomaly detection in RF cavities

Abstract

In order to accelerate charged particles and output them at a constant and controllable energy, modern particle accelerators make use of hollow, torus-like metal structures known as Radio Frequency (RF) cavities. An electromagnetic field is applied to the cavities, which in turn efficiently transfers the field’s energy to passing ions, accelerating them to a target speed. As newer accelerator designs are required to output particles at ever increasing energy levels, cavities are operated in their superconductive state in order to achieve a much higher accelerating gradient. The cavities are typically constructed as multiple cells, each consisting of two separate halves which are then welded together. The welding process heats up the surrounding cavity material, making it more susceptible to the formation of defective regions. Other types of anomalies can also manifest on the internal cavity surface, such as scratches caused by improper handling of the cavity and contamination from foreign objects. These anomalies are liable to affect the performance of the cavities through a process known as quenching, where defective regions experience an increase in temperature. This in turn heats up material surrounding the defect, bringing the cavity out of the superconductive state and greatly reducing the accelerating gradient. Well established diagnostic tests, such as the RF cold test used to locate these anomalies are available, but these require the cavity to be operated at its superconducting state, which is time consuming, expensive and requires multiple trained operators to perform. Instead, vision based systems which mark anomalies based on their physical appearance have been proposed as a quicker preliminary diagnostic tool. This work seeks to improve current cavity visual inspectors by proposing an optical system for a pre-existing prototype inspection robot located at the European Organisation for Nuclear Research (CERN). The system is able to scan the entire interior cavity surface at a high enough spatial resolution such that anomalies as small as 10µm in length can be reliably detected. As a full scan of each cavity produces several thousands of images, an automated anomaly detection and localisation model is employed. The model makes use of both high resolution edge features from a wavelet based detector to provide accurate localisation information while regecting false edges originating from noise, as well as contextual ones extracted from layers of a pre-trained neural network in order to detect the presence of anomalies. On the obtained cavity image dataset, the model achieved a sensitivity and specificity of 78% and 61% respectively, successfully identifying the anomalies most likely to affect the cavity performance.