Transfer learning allows pre-trained models to adapt to new scenarios with minimal retraining, enabling quicker deployment and reducing resource expenditure in manufacturing environments. By "transferring" knowledge learned from specific CFRP machining conditions, these models can identify key CFRP-specific patterns while significantly reducing the data required for training . However, the utilization of transfer learning under the context of CFRP machining is still lacking and further research is required to fully harness the potential of transfer learning, for enhancing model generalizability across diverse CFRP machining conditions. analysis. The proposed method was found to show higher prediction accuracy than BPNN, reaching a minimum RMSE of 3.45E-04. Li et al. developed a hybrid 1D CNN-Resnet network to predict the tool wear in CFRP milling and the structure of the network is shown in Fig. . The additional Resnet can use residual joints to map features to layer inputs and merge them with outputs. The proposed model achieved higher prediction accuracy than SVM and 1D-CNN, with minimum RMSE of 2.39.