Design and Experiment of Robotic Belt Grinding System with Constant Grinding Force

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.

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Calibration and accuracy analysis of robotic belt grinding system using the ruby probe and criteria sphere

Robotics and Computer-Integrated Manufacturing ◽

10.1016/j.rcim.2017.12.006 ◽

2018 ◽

Vol 51 ◽

pp. 189-201 ◽

Cited By ~ 16

Author(s):

Xiaohu Xu ◽

Dahu Zhu ◽

Jinshan Wang ◽

Sijie Yan ◽

Han Ding

Keyword(s):

Accuracy Analysis ◽

Belt Grinding ◽

Robotic Belt Grinding ◽

Grinding System

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A Normal Force Estimation Model for a Robotic Belt-grinding System

2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) ◽

10.1109/aim43001.2020.9158825 ◽

2020 ◽

Author(s):

Yu-Hsun Wang ◽

Yuan-Chieh Lo ◽

Pei-Chun Lin

Keyword(s):

Normal Force ◽

Force Estimation ◽

Estimation Model ◽

Belt Grinding ◽

Robotic Belt Grinding ◽

Grinding System

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Fixture Optimization for Robotic Belt Grinding System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.2030 ◽

2011 ◽

Vol 121-126 ◽

pp. 2030-2034

Author(s):

Dong Zhang ◽

Chao Yun ◽

Ling Zhang

Keyword(s):

Mathematical Model ◽

Optimization Methods ◽

Belt Grinding ◽

Robotic Grinding ◽

The Monte Carlo Method ◽

New Type ◽

Set Up ◽

Robotic Belt Grinding ◽

The Mathematical Model ◽

Grinding System

The precision is impacted when the robotic grinding path is discontinuous and the gripper needs to be replaced during manufacturing. In order to solve this problem, a new type PPPRRR grinding robot was proposed. The mathematical model for the robotic grinding paths was set up. The factors including the pose of the workpiece respect to the end joint and the position of contact wheel respect to the robot base frame {O}were analyzed to influence the grinding ability of the system. Base on the Monte Carlo method the posture and position factors above had been optimized, and the grinding ability of the system was increased. The optimization methods were proved right and workable by grinding golf head experiment.

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