Depth-wise separable convolutional neural-network-based intelligent chatter monitoring for thin-walled polish grinding

Abstract. To overcome the limitations of traditional convolutional neural networks in monitoring polishing and grinding chatter, this paper proposes a fusion approach combining deep separable convolutional neural networks (DCNNs) with gated recurrent units (GRUs). The grinding force signal undergoes preprocessing via an optimised variational model decomposition (VMD) algorithm and wavelet threshold denoising, with model hyperparameters optimised using a hybrid genetic–particle swarm algorithm (MGP). The designed squeeze-and-excitation (SE)–DCNN–GRU model employs deep separable convolution for multi-scale feature extraction, utilises the SE attention module to enhance key feature representation, and incorporates residual connections to address network degradation issues. Experiments demonstrate that this model achieves up to 98.8 % recognition accuracy for thin-walled titanium alloy components, maintaining over 95 % accuracy even under −5 dB noise conditions. Convergence speed increases by 39 % with significantly enhanced stability, providing an effective solution for chatter monitoring under complex operating conditions.