A study on pyramid-structured LSTM for milling tool wear prediction

Abstract

Accurate prediction of tool wear is critical for optimizing machining efficiency, minimizing unplanned downtime, and enabling predictive maintenance in intelligent manufacturing systems. However, developing computationally efficient models with high predictive accuracy remains a persistent challenge. To address this, we propose a novel pyramid-structured Long Short-Term Memory (LSTM) network for multi-step tool wear forecasting. The architecture features a hierarchical cascade of LSTM modules operating at progressively coarser temporal resolutions, enabling the model to capture both short-term fluctuations and long-term degradation trends. We further conduct a systematic ablation study to evaluate the impact of architectural depth and scale configuration on prediction performance. Experimental results demonstrate that the optimal model achieves a mean absolute percentage error below 0.5% on the validation set, a coefficient of determination close to 1, a mean absolute error of approximately 0.0005 mm, and a mean squared error lower than 5 × 10⁻⁶ mm², indicating excellent predictive performance. These results underscore the model’s high precision and robustness. The proposed framework provides a reliable, data-driven solution for tool condition monitoring and supports optimal tool replacement scheduling in smart manufacturing environments.