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Tool Wear State Monitoring in Titanium Alloy Milling Based on Wavelet Packet and TTAO-CNN-BiLSTM-AM

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Publicado en:Processes vol. 13, no. 1 (2025), p. 13
Autor principal: Yang, Zongshuo
Otros Autores: Li, Li, Zhang, Yunfeng, Jiang, Zhengquan, Liu, Xuegang
Publicado:
MDPI AG
Materias:
Accuracy
Deep learning
Titanium
Wavelet transforms
Artificial neural networks
Back propagation networks
Topology
Machine tools
Frequencies
Cutting tools
Manufacturing
Long short-term memory
Wear rate
Tool wear
Efficiency
Titanium base alloys
Research methodology
Fault diagnosis
Titanium alloys
Milling (machining)
Neural networks
Triangulation
Algorithms
Information processing
Heat resistance
Acceso en línea:Citation/Abstract
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Resumen:To effectively monitor the nonlinear wear variation of tools during the processing of titanium alloys, this study proposes a hybrid deep neural network fault diagnosis model that integrates the triangulation topology aggregation optimizer (TTAO), convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism (AM). Firstly, vibration signals from the machine tool spindle are acquired and subjected to the wavelet packet transform (WPT) to extract multi-frequency band energy features as model inputs. Then, the CNN and BiLSTM modules capture the features and temporal relationships of the input signals. Finally, introduction of the AM, combined with the TTAO algorithm, automatically extracts deep features, overcoming issues such as local optima and slow convergence in traditional neural networks, thereby enhancing the accuracy and efficiency of tool wear state recognition. The experimental results demonstrate that the proposed model achieves an average accuracy rate of 98.649% in predicting tool wear states, outperforming traditional backpropagation (BP) networks and standard CNN models.
ISSN:2227-9717
DOI:10.3390/pr13010013
Fuente:Materials Science Database