Convolutional Neural Network (CNN) is one of the most widely employed deep learning algorithms. A typical CNN architecture is illustrated in Fig. . Convolutional layers convolve the input with kernels to produce feature maps, and max pooling layers reduce the dimensionality of these feature maps. The resulting features are then passed to fully connected layers for prediction. The deep hierarchical architecture of CNNs enables multidimensional feature recognition, effectively mapping input data into a structured space that allows the model to represent high-level latent features . Therefore, CNN is widely used for local feature extraction in image and signal processing . Owing to these advantages, CNNs have been widely adopted as a feature engineering approach CFRP machining, including monitoring signal feature extraction , tool wear prediction and surface defects automatic recognition .