Design of Radial Basis Function Neural Network The network structure of Radial Basis Function uses the network structure of 4 nodes of the input layer, 10 nodes of the hidden layer, and 1 node of the output layer. Using the four eigenvalues â€‹â€‹(root mean square, variance, one-step autocorrelation coefficient, and maximum spectral peak value of 200-600 kHz) of the collected data (for a total of N samples) as the radial basis function input, the output layer node is a milling cutter The wear value. The fuzzy clustering fuzzy C-division method is used to transform the sample components into M groups. The cluster center of each group is used as the center of each basis function, and the basis function is Gaussian function. Then the variance of each center is calculated by the formula s=d/2M. Finally, each weight is calculated by the least squares method. 3 Experimental results The following processing conditions were used in the experiment: 1Ã˜4 high speed steel milling cutter, spindle speed 1120r/min, feed rate 15.68mm/min, depth of cut 5mm, workpiece using nodular cast iron (24HRC): ~ 03 high speed steel milling cutter, Spindle speed 900r/min, tool feed speed 12.6mm/min, depth of cut 3mm, workpiece using 45 # quenched and tempered steel (22HRC). The tool wear measuring tool is JGX-1 miniature tool microscope with an accuracy of 0.01mm. The four eigenvalues â€‹â€‹(root mean square value, variance, one-step autocorrelation coefficient, and maximum peak value (200-600 kHz)) of the collected 72 sets of data (36 sets for each cutting condition) were used as training samples. The design method of the radial basis function neural network obtains the required radial basis function neural network: then another set of 20 data (obtained under cutting condition 1) is used as a test sample. Figure 2 is a wear curve for 20 sets of data used for testing (10s for the test sample interval). In the picture The curve is the actually measured tool wear value, and the Î› curve is the value obtained by the radial basis function neural network.
Fig. 1 Radial basis function (RBF) network structure
Figure 2 Neural Network Test Curve
Table RBF network and BP network performance comparison training method Network type training time (s) Number of training steps Fuzzy-C clustering algorithm RBF network 1.20 7 Improved BP algorithm BP network 130.42 2016 Figure 3 is the radial basis function network output and the actual tool Wear value linear regression. In the figure, A is the actual wear value of the tool: T is the output of the RBF network. From the right table, the RBF network training speed is much higher than the BP network of the improved algorithm. 4 Conclusions Using the rms value, variance, one-step autocorrelation coefficient and maximum peak value (200-600kHz) of the acquired signal as feature quantities, the tool wear value can be estimated effectively. In real-time, on the microcomputer with a main frequency of 166MHz, the state recognition (35ms) from the data processing to the radial basis function neural network takes about 200ms in total. In practical applications, we collect data every time. The time interval can be set to 1s, 2s, etc. Therefore, this study can meet the real-time requirements.
â—‹ Data Points: - A=T:... Best Approximation Curve Figure 3 Linear Regression