Iterative learning control strategy for information model-based precision leadscrew grinding process

摘要

This paper puts forward a new compensation strategy for the generalized kinematic errors (GKEs) using off-line measuring information of the machined workpiece. An iterative learning control algorithm (ILCA) supported by the information model of machining complex is adopted to realize the strategy when the GKEs is produced by error motion of two relative motions, e.g. the helical errors of a lead screw. Experimental results show that the proposed GKEs compensation strategy and the information model based ILCA is effective for the precision leadscrew grinding process control.