Machine Learning Methods for Health-Index Prediction in Coating Chambers
This work addresses maintenance optimization for coating chamber manufacturers, but it is incremental as it builds on existing condition-based maintenance approaches with a specific domain application.
The paper tackled predicting the future condition of coating chambers in jewelry production to optimize maintenance, by deriving a novel health indicator from process data and evaluating machine learning algorithms, with decision tree models outperforming benchmarks by at least 0.22 in mean average error.
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.