ResNet-based multi-feature map fusion for classification of drilling surface roughness
The traditional five-face composite computerized numerical control(CNC)drilling surface roughness measurement is complicated,and there is a large human error in manual measurement.The traditional multiple regression and polynomial fitting methods only use rotational speed and feed speed parameters with low data utilization and high noise sensitivity;traditional machine learning can not effectively extract the deep and complex features of the signal.Aiming at the above problems,a classification and prediction method of drilling surface roughness based on ResNet model,fusion of spectrogram features and time-frequency graph features was proposed.Firstly,the process parameter variables of the CNC drilling processing experiment were determined according to the theory of CNC drilling processing and the actual CNC drilling experience of the enterprise.Secondly,a multi-source data acquisition system was developed based on SYNTEC CNC system,and the drilling process data were collected in real time.Then,the spectral and time-frequency characteristics of the three-axis vibration signals were analyzed,and the correlation between the vibration signals and the surface roughness category was verified.Then,the Kalman filtering was used for noise reduction of the three-axis vibration signals,and the fast Fourier transform(FFT)and the continuous wavelet transform(CWT)were used to convert the spectro-thermograms and time-frequency maps of the vibration signals,and matrix splicing was used to splice and merge the uniaxial time-frequency maps of the three-axis vibration signals to get the three-axis vibration time-frequency map.Finally,the fusion of spectral and time-frequency features was realized by convolving the spectral heat map and time-frequency map,and the comparison experiments between ResNet and other network models such as Densenet,Shufflenet and Mobilenet_v3_small were carried out.The research results show that the correct rate of surface roughness classification based on the ResNet network model is improved by about 9%relative to the other network models mentioned above,and the correctness of the three-axis time-frequency feature fusion as well as the fusion method of spectral and time-frequency features is also verified.Due to the low cost of model training and fast training convergence,the method has a good prospect for industrial application in lightweight and low-cost prediction and classification of surface roughness of drilling on CNC machine tools.
万方数据
维普
