  Coating chambers create thin layers that improve the mechanical and optical
surface properties in jewelry production using physical vapor deposition. In
such a process, evaporated material condensates on the walls of such chambers
and, over time, causes mechanical defects and unstable processes. As a result,
manufacturers perform extensive maintenance procedures to reduce production
loss. Current rule-based maintenance strategies neglect the impact of specific
recipes and the actual condition of the vacuum chamber. Our overall goal is to
predict the future condition of the coating chamber to allow cost and quality
optimized maintenance of the equipment. This paper describes the derivation of
a novel health indicator that serves as a step toward condition-based
maintenance for coating chambers. We indirectly use gas emissions of the
chamber's contamination to evaluate the machine's condition. Our approach
relies on process data and does not require additional hardware installation.
Further, we evaluated multiple machine learning algorithms for a
condition-based forecast of the health indicator that also reflects production
planning. Our results show that models based on decision trees are the most
effective and outperform all three benchmarks, improving at least $0.22$ in the
mean average error. Our work paves the way for cost and quality optimized
maintenance of coating applications.
