Method for improving robotic surface milling accuracy by stiffness optimization and force-induced error compensation
Sicheng Zhao, Guoqing Liu, Renzhe Wei, Guohui Yang, Jinbo NiuPurpose
In robotic surface milling, the inherently low stiffness of industrial robots often leads to significant force-induced deformation errors and reduced machining accuracy. This study aims to propose a multi-factor optimization method that combines stiffness optimization with force-induced error compensation to improve surface milling accuracy.
Design/methodology/approach
First, the joint stiffness of the robot was identified by a static loading experiment, and stiffness performance was evaluated using the volume of the flexibility ellipsoid. A step-by-step algorithm combining discrete search and graph optimization was then used to resolve redundancy and improve the overall stiffness performance of the tool path. Then, an error prediction model coupling milling force and spindle gravity was established. On the basis of stiffness optimization, the force-induced error is predicted and compensated for by the mirror iteration method.
Findings
Three robotic surface milling experiments were conducted at two axial depths of cut, including two tests at 0.5 mm and one test at 0.7 mm. In all experiments, the integrated method achieved the lowest mean absolute error, maximum absolute error and root mean square error among the tested paths. The results show that combining stiffness optimization with force-induced error compensation reduced machining errors more effectively than either individual module.
Originality/value
This study proposes an integrated compensation method for multiple error sources that combines stiffness optimization and force-induced error compensation, together with a step-by-step algorithm for efficiently solving the global optimization model. The proposed method was validated through robotic surface milling experiments.