Pyramid Pooling‑Based Vision Transformer for Tool Condition Recognition

This study focuses on tool condition recognition through data-driven approaches to enhance the intelligence level of computerized numerical control （CNC） machining processes and improve tool utilization efficiency. Traditional tool monitoring methods that rely on empirical knowledge or limited mathematical models struggle to adapt to complex and dynamic machining environments. To address this， we implement real-time tool condition recognition by introducing deep learning technology. Aiming to the insufficient recognition accuracy， we propose a pyramid pooling-based vision Transformer network （P2ViT-Net） method for tool condition recognition. Using images as input effectively mitigates the issue of low-dimensional signal features. We enhance the vision Transformer （ViT） framework for image classification by developing the P2ViT model and adapt it to tool condition recognition. Experimental results demonstrate that our improved P2ViT model achieves 94.4% recognition accuracy， showing a 10% improvement over conventional ViT and outperforming all comparative convolutional neural network models.