Transformer-Based Vision-CNC Fusion for Real-Time Error Compensation and Adaptive Optimization in Intelligent Machining Systems

It is difficult for current intelligent machining systems to accurately compensate for machining errors caused by thermal deformation of the spindle and fixture offset. This paper introduces a transformer-based vision-computerized numerical control fusion optimization model, combines computer vision technology with the computerized numerical control system, and drives error adaptive compensation through visual monitoring to improve machining accuracy and system stability. The experimental results show that the compensation effect of the model under different working conditions is significant, the error correction ability in the processing of various workpieces is strong, and the average precision is 87.64%. In ultra-high-speed mode, path optimization reduces the processing time to 180 s and the tool wear to 10.5 μm. The research in this paper provides an optimization method for intelligent machining, which has broad application prospects.