Vision transformer-based adaptive optimization for CNC machining with real-time quality monitoring

Achieving optimal machining quality while maintaining production efficiency in computer numerical control (CNC) operations remains challenging due to complex process dynamics and limited real-time quality feedback. This paper presents a Vision Transformer-based adaptive optimization system that integrates real-time quality monitoring with closed-loop parameter control for CNC machining. The system employs a Vision Transformer architecture with multitask learning heads to simultaneously assess surface roughness, dimensional accuracy, and defect presence, coupled with a model predictive control framework featuring adaptive gain modulation for dynamic parameter adjustment. Experimental validation on the NEU Surface Defect Database and real machining operations demonstrates that the proposed approach achieves 94.7% F1-score in quality assessment, surpassing CNN-based methods (ResNet50: 89.8%) and other transformer variants (Swin-T: 92.0%). The closed-loop optimization delivers 44.1% surface roughness improvement with concurrent 12.3% efficiency gains, while maintaining real-time performance at 54ms latency (18.5 FPS). Ablation studies confirm that the Vision Transformer contributes 23.5 percentage points improvement in detection accuracy, while adaptive optimization adds 5.7 percentage points to surface quality enhancement. The system represents a significant advancement toward intelligent, self-optimizing manufacturing processes capable of autonomous quality maintenance under dynamic operating conditions.