Fig 1.
Generative Adversarial Network.
Fig 2.
Convolutional Neural Network.
Fig 3.
LSTM Network Structure.
Fig 4.
Prediction framework for circular saw blade wear.
Fig 5.
Basic structure model of saw blade.
Table 1.
J-C constitutive model parameters.
Table 2.
J-C damage model parameters.
Table 3.
Basic physical properties of metal ceramic saw blades.
Fig 6.
Equivalent force cloud map.
Fig 7.
Sawtooth mesh distortion.
Fig 8.
Experimental data collection platform.
Fig 9.
The raw samples of vibration signals.
Fig 10.
Overlapping sampling process.
Fig 11.
Overlapping sampling data samples.
Fig 12.
Comparison of data before and after denoising using single-layer wavelet transform.
Fig 13.
Comparison of data before and after normalization.
Fig 14.
PCC-optimized GAN model.
Fig 15.
CNN-LSTM model based on dual feature fusion.
Table 4.
The types of circular saw blade failures.
Table 5.
Calibrated serration wear values for each condition.
Fig 16.
Measurement of saw blade wear value.
Fig 17.
PCC R-value cloud map.
Fig 18.
PCC P-value cloud map.
Table 6.
Correlation assumptions identified for different R-value ranges.
Table 7.
The hyperparameter settings for the PCC-optimized GAN.
Fig 19.
Analysis of the effect of correlation coefficient.
Fig 20.
PCC R-value cloud map.
Fig 21.
PCC P-value cloud map.
Table 8.
The relevant hyperparameter settings for the recognition model.
Fig 22.
Training set prediction results.
Fig 23.
Training set identification confusion matrix.
Fig 24.
Test set prediction results.
Fig 25.
Test set identification confusion matrix.
Fig 26.
Confusion Matrix of LSTM.
Fig 27.
Confusion Matrix of RBFNN.
Fig 28.
Performance Comparison of Models.