Multi-channel chatter detection in robotic milling based on successive multivariate variational mode decomposition and hybrid deep convolutional neural network

Chatter is a critical factor that limits machining quality and production efficiency in robotic milling. Therefore, timely and accurate online chatter detection is essential for achieving high-precision and high-efficiency machining. However, the dynamic properties of milling robots are highly pose-dependent, which significantly increases the randomness and complexity of chatter. Furthermore, due to the strong nonlinearity and flexibility of milling robots, chatter induces coupled three-dimensional vibrations. As a result, relying on vibration signals from a single direction is insufficient for achieving reliable chatter detection. In this paper, a multi-channel online chatter detection method in robotic milling is proposed, based on successive multivariate variational mode decomposition (SMVMD) and hybrid deep convolutional neural network. First, the SMVMD method combined with a dimensionless chatter index is employed to adaptively extract the chatter components in the three-channel acceleration signals. Then, the energy ratio sequence between the reconstructed chatter signal and the milling signal is calculated. Finally, a hybrid deep convolutional neural network (ICR-DCN), composed of Inception, convolutional block attention module (CBAM), residual network (ResNet) and classification module, is constructed to automatically extract chatter features from the energy ratio sequence and achieve online chatter detection. Experimental results demonstrate that the proposed method can achieve high-precision online chatter detection across variations in different robotic poses, cutting parameters, workpiece materials and tools. Comparative studies further validate the accuracy and stability of the proposed method across various scenarios.