A novel selected force controlling method for improving robotic grinding accuracy of complex curved blade. (October 2022)

• Record Type: Journal Article
• Authors: Wang, Ziling; Zou, Lai; Luo, Guoyue; Lv, Chong; Huang, Yun
• Is Part Of: ISA transactions. Volume 129(2022)Part A
• Page Start: 642
• Page End: 658
• Publication Date: 2022-10
• Subjects: Robotic grinding accuracy -- Selected force controlling -- Adaptive impedance controller -- Regional division -- Neural network -- Complex curved blade; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.12.032
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: 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. Highlights: The developed SFC method used for improving robotic grinding accuracy is essentially consisted of the regional division and force controller. Regional division (RD) is mainly determined by similar ideal normal grinding force values of blade surface profile, which are calculated based radius of principal curvature and residual distribution at corresponding CC points. AICNN force controller is achieved by developed adaptive impedance controller and proposed neural network online compensation algorithm, which is used to compensate force control errors in real time and control the normal grinding force.