A Systematic Model of Machining Error Reduction in Robotic Grinding

Abstract

Robotic grinding is a promising automatic technique for free-form surface manufacturing. One important problem that restrains the application of robotic grinding is the machining quality. Existing methods considers only individual kinematic errors or joint stiffness. In this article, a systematic method of error compensation, workpiece position optimization and tool pose optimization is proposed to reduce the machining error. First, the mathematical models of machining error with respect to both kinematic errors and joint stiffness are built using speed and force adjoint transformation, respectively. Compared with existing indirect index evaluation models, this model is improved by directly building the quantitative function relationship between the machining error and the corresponding factors. Based on the model, an error compensation strategy is presented by only fine-tuning the workpiece frame position. Then, an objective function based on the compensated machining error is defined to optimize both the workpiece position and the tool pose. Experiments demonstrate the availability of the proposed method for machining error reduction.