The application belongs to the technical field of
intelligent control, and particularly relates to a bearing grinder high-precision control method based on
artificial intelligence. The method solves the problems of traditional bearing grinder fixed parameter
processing, cross-process non-linkage, PID control precision deficiency and the like. The method first constructs and trains three types of models of
grinding quality image recognition,
grinding parameter sensitivity and cross-process progressive parameter adaptive prediction; through coarse
grinding, semi-fine grinding and fine grinding processes, grinding
quality characteristics are extracted through the image recognition model and
processing errors are generated, error parameter tracing is completed in combination with the sensitivity model, and each process optimal grinding parameter is output by the prediction model; then, the improved grey wolf
algorithm of elite level and reverse escape cooperative guidance is used to optimize the
PID controller, so as to realize grinding parameter closed-
loop control. The application realizes cross-process parameter linkage optimization, improves parameter regulation pertinence and control precision, improves bearing grinding precision consistency, reduces
rework rate, and adapts to high-end bearing precision grinding demand.