Robot machining of thin-walled workpieces with automatically reconfigurable fixturing through feature analysis

Abstract

Machining of large scale thin-walled workpieces pose high requirements for fixturing because of large stiffness changes due to material removal. Automatically reconfigurable fixtures that allow in-process reconfiguration are a promising approach. In this paper, an efficient approach based on feature analysis is proposed for fixture reconfiguration planning. For this, material removal is simulated. At discrete points in time, feature analysis is performed and an approximated local stiffness map is calculated with weight functions derived from the features’ parameterizations. Viable fixture configurations are then identified through root finding and validated through simulation in Ansys and experimentally with a fixture jig. In both validations, workpiece deformation for drilling operations could be reduced to an acceptable level enabling the implementation's future use in industrial application.