Machine Learning Methods for Health-Index Prediction in Coating Chambers

TL;DR

This paper develops a machine learning approach to predict the health of coating chambers in jewelry manufacturing, enabling proactive maintenance to reduce costs and improve quality.

Contribution

It introduces a novel health indicator derived from process data and evaluates multiple ML algorithms, identifying decision trees as the most effective for condition forecasting.

Findings

Decision tree models outperform benchmarks in accuracy.

The health indicator effectively predicts chamber condition.

Approach does not require additional hardware.

Abstract

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.