Abstract
The accurate monitoring of tool condition is of great significance to improve the machining quality and efficiency of parts, prolong the service life of tools and machine tools, and reduce the harm of manufacturing environment. In this dissertation, two methods based on synchronous compressed continuous wavelet transform and deep convolution neural network (SWT-DCNN) and synchronous compressed continuous wavelet transform and deep convolution neural network (SST-DCNN) are proposed to monitor tool wear. It is found that the recognition accuracy of SWT-DCNN method is 99.96%, and that of SST-DCNN method is 99.86%. The reason is that SWT method has good time-frequency energy aggregation. Compared with the SST-DCNN method, the recognition accuracy of the SWT-DCNN method is more stable. At the same time, it is found that the recognition rate of the SST-DCNN method in the process of normal tool monitoring is only 93.3%, which is easy to classify normal tools into the initial wear category. The experimental results show that the two methods proposed in this paper can effectively monitor the tool wear state.
Highlights
With the development of intelligent manufacturing technology, the process system is required to further improve the ability of active perception and independent decisionmaking. erefore, tool condition monitoring has become a research hotspot in the machining field. e accurate monitoring of tool condition is of great significance to improve the machining quality and efficiency of parts, prolong the service life of tools and machine tools, and reduce the harm of manufacturing environment
It includes 3 volume layers, 3 pool layers, 1 input layer, and full connection layer. e Adam adaptive optimizer is used to continuously update the network training parameters. e initial learning rate is 0.0001, and the attenuation rate is 0.9. 25 samples are used as a batch input convolutional neural network for training, and the number of iterations is 10. e cross entropy loss function is used to detect the training state of the convolutional neural network. e error function calculates the loss value of each iteration according to the error between the actual value and the expected value during the training period
Sample data under different tool states are converted into time spectrum by synchronous compressed short-time Fourier transform and synchronous compressed continuous wavelet transform, as shown in Figures 5 and 6
Summary
With the development of intelligent manufacturing technology, the process system is required to further improve the ability of active perception and independent decisionmaking. erefore, tool condition monitoring has become a research hotspot in the machining field. e accurate monitoring of tool condition is of great significance to improve the machining quality and efficiency of parts, prolong the service life of tools and machine tools, and reduce the harm of manufacturing environment. Rizal et al [14] realized tool wear detection based on the time-frequency domain characteristics of cutting force, vibration, torque, and temperature signals combined with Martin’s system. Liu et al [15] realized the detection of different machining states based on the time-frequency domain characteristics of force and vibration signals and support vector machine. Huang et al [18] performed short-time Fourier transform on the vibration data in the cutting process to obtain the time-frequency domain diagrams under different tool states and combined with the convolutional neural network to monitor the tool state. The deep neural network is trained with the time-frequency domain Atlas with different tool wear conditions, and two methods of automatically monitoring tool wear are obtained
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