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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 time–frequency domain methods and Markov transition field methods. Sensitivity analysis of time–frequency domain features is conducted using Pearson correlation coefficient analysis. A hybrid neural network model (DBMA) for chatter detection is constructed by integrating dual-scale parallel convolutional neural networks, bidirectional gated recurrent units, and multi-head attention mechanisms. The Ivy algorithm is utilized to optimize the hyperparameters of DBMA. The t-SNE algorithm is employed to visualize features extracted from different network layers of the chatter detection model. Results demonstrate that effective denoising of machining signals and the use of multi-modal data can significantly improve the accuracy of state detection. Compared with other methods, the proposed model exhibits superior stability and robustness. [ABSTRACT FROM AUTHOR]

Based on density functional theory calculations, this study analyzed the gas-sensing performance of Ti3C2Tx (T=O, F, OH) monolayers modified with precious metal atoms (Ag and Au) for HCHO and C6H6 gas molecules. Firstly, stable structures of Ag- and Au-single-atom doped Ti3C2Tx (T=O, F, OH) surfaces were constructed and then HCHO and C6H6 gas molecules were set to approach the modified structures at different initial positions. The most stable adsorption structure was selected for further analysis of the adsorption energy, adsorption distance, charge transfer, charge deformation density, total density of states, and partial density of states. The results show that the Ag and Au modifications improved the adsorption performance of Ti3C2O2 for HCHO and C6H6. In comparison, the effect of the Au modification was better than that of Ag. For Ti3C2F2, the Ag and Au doping modifications did not significantly change the adsorption effects for HCHO and C6H6. However, the Ag and Au doping modifications decreased the adsorption of Ti3C2(OH)2 for HCHO, while there was no significant change in the gas adsorption for C6H6. The above results serve as a theoretical foundation for the design of new sensors for HCHO and C6H6. [ABSTRACT FROM AUTHOR]