Abstract
To improve the processing quality and efficiency of robotic belt grinding, an adaptive sliding-mode iterative constant-force control method for a 6-DOF robotic belt grinding platform is proposed based on a one-dimension force sensor. In the investigation, first, the relationship between the normal and the tangential forces of the grinding contact force is revealed, and a simplified grinding force mapping relationship is presented for the application to one-dimension force sensors. Next, the relationship between the deformation and the grinding depth during the grinding is discussed, and a deformation-based dynamic model describing robotic belt grinding is established. Then, aiming at an application scene of robot belt grinding, an adaptive iterative learning method is put forward, which is combined with sliding mode control to overcome the uncertainty of the grinding force and improve the stability of the control system. Finally, some experiments were carried out and the results show that, after ten times iterations, the grinding force fluctuation becomes less than 2N, the mean value, standard deviation and variance of the grinding force error’s absolute value all significantly decrease, and that the surface quality of the machined parts significantly improves. All these demonstrate that the proposed force control method is effective and that the proposed algorithm is fast in convergence and strong in adaptability.
Highlights
As a finishing process, abrasive belt grinding achieves high material removal rates, and can be used to improve the surface roughness of components [1]
By integrating a multi-degree industrial robot as a manipulator, a flexible manufacturing cell can be formed, which is especially suitable for processing surfaces with complicated geometries, such as turbine blades or faucets [2]
There have been many studies on robotic belt grinding, and some of them have addressed the problems of robotic offline programming [5,6] and robotic trajectory planning [7,8,9]
Summary
Abrasive belt grinding achieves high material removal rates, and can be used to improve the surface roughness of components [1]. Lu et al [20] proposed a sliding-mode-based impedance controller with a continuous PI control near the surface of the sliding mode to avoid chatter and reduce steady-state errors These traditional force control strategies can achieve certain control effects, due to the non-linearity and a large amount of uncertainty in robotic belt grinding, it is difficult to achieve satisfactory results using these control methods. A self-adaptive sliding-mode iterative learning method for robotic belt grinding constant-force control is proposed It can effectively compensate for the error caused by the uncertainty of robotic belt grinding, and has flexible parameter settings and is suitable for actual grinding. Feasibility ofthe thecontrol simplified force-mapping relationship is used to grind angle steel to verify the feasibility of algorithm The effectiveness of this approach are further proven by a curved-surface workpiece grinding experiment. The feasibility of the simplified force-mapping relationship are further proven by a curved-surface workpiece experiment
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