Multi-Axis Acceleration Response Prediction in Milling: An Artificial Intelligence-Based Approach

Vibrations generated during cutting in machining processes present a significant challenge, directly impacting surface quality, tool longevity, and processing efficiency. Accurate modeling of vibration behavior under varying cutting conditions is therefore essential for advancing the understanding of machining dynamics. In this research, triaxial vibration accelerations in the tool holder region during CNC milling of Al 6061 material were experimentally measured. The proposed framework focuses on the prediction of process-level time-domain acceleration responses rather than high-frequency chatter or tooth-passing vibration components. The experiments incorporated a range of spindle speeds, feed rates, and depths of cut. The resulting multi-axial acceleration time series were modeled using deep learning-based time series approaches to capture complex and nonlinear dynamics. Specifically, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures were developed, and their generalization capabilities were assessed using the Leave-One-Experiment-Out (LOEO) method. Comparative analyses employing root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) metrics indicate that both models reliably predict multi-axis acceleration responses. Notably, the LSTM architecture demonstrates a more balanced learning performance for representing long-term dynamics. These findings provide an effective, practical approach to data-driven modeling of vibrations in CNC milling processes.