A Practical Hybrid Framework for RUL Prediction in Ion Mill Etching by Integrating Operational States

Recent deep learning approaches for Remaining Useful Life (RUL) prediction in ion mill etching have achieved remarkable performance. State-of-the-art models, particularly those based on the Transformer architecture, have reached high prediction accuracy by exclusively training on data from the primary operational state. However, this strategy discards data from ancillary operational states, failing to address the critical challenge of providing the continuous RUL predictions required for practical applications.This research highlights the limitations of existing methods, which create "prediction gaps," and proposes a state-aware hybrid framework that considers the complete operational profile of the equipment. We apply a high-performance deep learning model for the primary operational state to ensure our predictive accuracy is competitive with state-of-the-art methods. The core contribution of our work, however, is the systematic design and validation of estimation logic for the previously ignored ancillary states. Specifically, we integrate the primary deep learning model with rule-based methods and simpler statistical models that activate during these ancillary states. This hybrid approach enables a shift from a competition based on accuracy under ideal conditions to the development of a robust and practical RUL prediction system. This paper argues for the importance of an integrated predictive framework that covers the full equipment lifecycle, rather than focusing on a single, complex model for a subset of data.