Dynamic milling force model considering vibration and tool flank wear width for monitoring tool states in machining of Ti-6AI-4V

Ma, Junjin; Zhang, Yujie; Jiao, Feng; Cui, Xiaobin; Zhang, Dinghua; Ren, Lei; Zhao, Bo; Pang, Xiaoyan

doi:10.1016/j.jmapro.2024.07.022

Journal of Manufacturing Processes

2024

DOI: 10.1016/j.jmapro.2024.07.022

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Dynamic milling force model considering vibration and tool flank wear width for monitoring tool states in machining of Ti-6AI-4V

Junjin Ma,

Yujie Zhang,

Feng Jiao

et al.

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

Yang,

Li,

Zhang

et al. 2024

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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.

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

Yang,

Li,

Zhang

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

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Dynamic milling force model considering vibration and tool flank wear width for monitoring tool states in machining of Ti-6AI-4V

Cited by 1 publication

References 53 publications

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

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

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