A novel selected force controlling method for improving robotic grinding accuracy of complex curved blade

Nonlinear time-varying contact state is a crucial factor to prevent the traditional robotic belt grinding method from precision machining of blade. In this case, a novel selected force controlling method (SFC) with consideration of regional division (RD) based on machining allowance is proposed for improving robotic grinding accuracy of complex curved blade, on basis of the self-developed adaptive impedance controller. Ideal normal grinding force at each cutter-contact (CC) point is calculated by principal curvature radius and regional allowance of blade surface. Then, the CC points with similar ideal normal grinding force are divided into one region along grinding path based on the force threshold. Furthermore, an adaptive impedance controller with neural network online compensation algorithm (AICNN) is developed, and the verification test results of grinding four profile areas of intake side, exhaust side, convex and concave, indicate that the force control accuracy with AICNN has increased by 80.33%, 50.58%, 82.65% and 69.01% than that without the controller, respectively. Based on this, the grinding experiment of typical turbine blade is conducted with SFC, and the surface profile accuracy values at the four profile areas have evidently improved by 48.79%, 35.67%, 59.54%, and 66.90% than that with conventional grinding (CG), respectively.