Machine Learning-Enhanced Prediction of Surface Smoothness for Inertial
  Confinement Fusion Target Polishing Using Limited Data
Antonios Alexos Junze Liu Akash Tiwari Kshitij Bhardwaj Sean Hayes Pierre Baldi Satish Bukkapatnam Suhas Bhandarkar
In Inertial Confinement Fusion (ICF) process, roughly a 2mm spherical shell
made of high density carbon is used as target for laser beams, which compress
and heat it to energy levels needed for high fusion yield. These shells are
polished meticulously to meet the standards for a fusion shot. However, the
polishing of these shells involves multiple stages, with each stage taking
several hours. To make sure that the polishing process is advancing in the
right direction, we are able to measure the shell surface roughness. This
measurement, however, is very labor-intensive, time-consuming, and requires a
human operator. We propose to use machine learning models that can predict
surface roughness based on the data collected from a vibration sensor that is
connected to the polisher. Such models can generate surface roughness of the
shells in real-time, allowing the operator to make any necessary changes to the
polishing for optimal result.