Optimal planning of an adaptively controlled robotic disk grinding process

In this paper, an adaptive control optimization (ACO) robotic grinding system is described. An industrial robot equipped with a pneumatic grinder, a six degree-of-freedom force sensor, and two non-contact inductive proximity sensors inter-linked with a PC are the integrated part of this system. The system is designed to grind the workpiece with maximum possible metal removal rate (MRR) within the constraints on workpiece burn and surface finish which, in turn, depend on the degree of disk dullness. A model-based approach has been used for the optimization. To perform the cutting operation, normal grinding force and robot feed speed have been selected as control parameters. The normal grinding force is effectively tracked on-line using an Adaptive Generalized Predictive Controller (GPC) algorithm, which was experimentally tested. The optimal robot feed speed is tracked due to the robot system's capability to accept on-line end effector path modifications by means of the robot's “ALTER” command. This system is able to plan and efficiently execute a multi-pass grinding sequence for removing large amounts of unwanted material. Several illustrative results are presented that confirm the practical feasibility of the optimization concept and demonstrate the performance of the ACO robotic grinding system.