Mamba-TSMixer Model for Tool Chatter Prediction Based on Spindle Acoustic Emission Signal Fusion - Kuśowa, Karolina Tatiana; Lupa, Ludwik (Journal of Applied Automation Technologies, 2025)
Reliable warning of regenerative chatter is still an issue because spindle speed, engagement and tool wear change the observable vibration signature. Develop a causal Mamba-TSMixer model in this paper that integrates spindle-mounted acoustic-emission data with rotational and process context for horizon-based chatter prediction. Raw emission signals are converted into synchronous envelope, impulsiveness, spectral and order-aware descriptors. A selective state-space path retains the extended-time dependency; a temporal-channel mixer handles local cross-feature correlations; and cross-gated fusion avoids context-inconsistent emission bursts. There are 1,260 milling experiments, five speed ranges, four wear conditions and two unseen-range experiments. At a prediction horizon of 200ms, the model has an F1 score of 0.943 for events, an area under the receiver operating characteristic curve (AUC) of 0.961, and a median warning lead of 184ms. Compared with the best-performing baseline, event F1 is up by 3.8% and false alarms have dropped from 0.31 to 0.18 per cutting minute. Performance is still 0.912 F1 after masking one group of acoustic-emission features, and causal inference needs 1.8ms per update. Ablation shows that selective memory and gated fusion contribute to the gain independently. Run the grouped split and retention speed-wear combinations to further test whether the predictor has exceeded the familiar cutting adjacent window. Based on the above experiments, high-frequency spindle emission can be used as an early warning indicator under different machining conditions, and models have been developed for both long-term and short-term analysis. The constructed structure offers a high-performance platform for the unstable operating environment of real-time monitoring.