Programming deburring robots based on human demonstration with direct burr size measurement

This paper discusses a new method for teaching a deburring robot based on demonstration of human skilful motion. The robot is programmed to adjust the tool feedrate in accordance with the varying burr characteristics, such as burr size and material properties. This dynamic change of tool feedrate is motivated by the effective human skill in performing a deburring task. The relationship between the tool feedrate and burr characteristics is obtained from human demonstration data and stored in a computer as an associative memory. This associative memory enables the robot to select the tool feedrate that well matches the burr characteristics. Therefore, the robot motion is always effective in removing burrs and generating smooth finish of workpiece surface without severe tool wear. In order to identify burr characteristics, a laser displacement sensor has been used for direct burr height measurement, and a deburring process model has been applied for material property differentiation. The learned associative memory is stored and represented by a neural network, which can be easily incorporated into robot programming. Experimental results show that a robot can perform a deburring task in a manner similar to its human teacher