The precision and quality of machining in computer numerical control(CNC)machines are significantly impacted by the state of the tool.Therefore,it is essential and crucial to monitor the tool’s condition in real time...The precision and quality of machining in computer numerical control(CNC)machines are significantly impacted by the state of the tool.Therefore,it is essential and crucial to monitor the tool’s condition in real time during operation.To improve the monitoring accuracy of tool wear values,a tool wear monitoring approach is developed in this work,which is based on an improved integrated model of densely connected convolutional network(DenseNet)and gated recurrent unit(GRU),which incorporates data preprocessing via wavelet packet transform(WPT).Firstly,wavelet packet decomposition(WPD)is used to extract time-frequency domain features from the original timeseries monitoring signals of the tool.Secondly,the multidimensional deep features are extracted from DenseNet containing asymmetric convolution kernels,and feature fusion is performed.A dilation scheme is employed to acquire more historical data by utilizing dilated convolutional kernels with different dilation rates.Finally,the GRU is utilized to extract temporal features from the extracted deep-level signal features,and the feature mapping of these temporal features is then carried out by a fully connected neural network,which ultimately achieves the monitoring of tool wear values.Comprehensive experiments conducted on reference datasets show that the proposed model performs better in terms of accuracy and generalization than other cutting-edge tool wear monitoring algorithms.展开更多
基金supported by the National Natural Science Foundation of China(62020106003,62273177,62233009)the Natural Science Foundation of Jiangsu Province of China(BK20222012)+2 种基金the Programme of Introducing Talents of Discipline to Universities of China(B20007)the Fundamental Research Funds for the Central Universities(NI2024001)the National Key Laboratory of Space Intelligent Control(HTKJ2023KL502006).
文摘The precision and quality of machining in computer numerical control(CNC)machines are significantly impacted by the state of the tool.Therefore,it is essential and crucial to monitor the tool’s condition in real time during operation.To improve the monitoring accuracy of tool wear values,a tool wear monitoring approach is developed in this work,which is based on an improved integrated model of densely connected convolutional network(DenseNet)and gated recurrent unit(GRU),which incorporates data preprocessing via wavelet packet transform(WPT).Firstly,wavelet packet decomposition(WPD)is used to extract time-frequency domain features from the original timeseries monitoring signals of the tool.Secondly,the multidimensional deep features are extracted from DenseNet containing asymmetric convolution kernels,and feature fusion is performed.A dilation scheme is employed to acquire more historical data by utilizing dilated convolutional kernels with different dilation rates.Finally,the GRU is utilized to extract temporal features from the extracted deep-level signal features,and the feature mapping of these temporal features is then carried out by a fully connected neural network,which ultimately achieves the monitoring of tool wear values.Comprehensive experiments conducted on reference datasets show that the proposed model performs better in terms of accuracy and generalization than other cutting-edge tool wear monitoring algorithms.
