Neural Network-Based Defect Detection on Tomography Images for Quality Control of Inertial Fusion Capsules

Abstract

Recent headlines of record-breaking fusion energy yields using inertial confinement have led to a growing interest in the metrology processes used to inspect the high-density carbon (HDC) capsules that have achieved these yields. To ensure the highest quality HDC shells, the tomographic reconstructions are reviewed for defects to satisfy a certain standard of quality. Historically, a human expert would visually analyze HDC tomography data searching for defects, which is very time-consuming and labor-intensive. While classical computer vision (CCV) methods have also been employed, machine-learning methods have demonstrated the ability to analyze vast amounts of data at high speeds and more robustly identify patterns and anomalies that may indicate the presence of previously undetectable defects. Presented here is our novel process, ShellNet, that has been developed to automate this defect detection task on HDC shells using convolutional neural networks (CNNs); the challenges and limitations of this approach are also discussed. A machine-learning model trained on thousands of expert-labeled defects and tomography images, utilizing a custom labeling tool, is now being used in production. Case studies are presented, demonstrating the effectiveness of machine learning in this application, as well as outlines for future research in this area. Overall, machine learning has been shown to be an effective method in enhancing the quality-based selection process of HDC shells, even exceeding the recall performance of human-labeled data. These improvements promise to lead to overall improved reliability of HDC materials in inertial fusion technology.