Multi-modal denoised data-driven milling chatter detection using an optimized hybrid neural network architecture.

ABSTRACT: Chatter, a type of self-excited vibration, deteriorates surface quality and reduces tool life and machining efficiency. Chatter detection serves as an effective approach to achieve stable cutting. To address the low accuracy in chatter detection caused by the limitations of both one-dimensional temporal and two-dimensional image modal information, this study proposes a multi-modal denoised data-driven milling chatter detection method using an optimized hybrid neural network architecture. A data denoising model combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Singular Value Decomposition (SVD) is established. The Ivy algorithm is employed to optimize the hyperparameters of CEEMD-SVD. Multi-modal data features of different machining states are then obtained using tim

SUBMITTER: Gao H 

PROVIDER: S-EPMC11785781 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature