Workpiece Position Optimisation in Robotic Multi-Axis Machining

Abstract

The use of robots for machining is becoming more and more common in industrial robotics applications. The advantages include lower acquisition costs compared to CNC machines and a larger working space with respect to the machine footprint. The disadvantages are low static stiffness and the risk that the robot structure will emit low-frequency vibrations during the machining operation. Both of these phenomena negatively affect the accuracy and quality of the machined part. In this paper, a mathematical model of the static stiffness of an industrial robot is developed from experimentally measured data, and further implemented in the off-line preparation of a robot control programme. By determining the directional stiffness during machining operations and calculating an integral stiffness criterion for a given robot configuration and workpiece position in the workspace, a genetic algorithm is used to find the optimal part position and robot end-effectors' redundant angle of rotation. The model’s validity and accuracy are verified by a five-axis machining experiment. The results of measuring the quality of the surfaces machined in the default and optimised workpiece positions clearly show the effectiveness of the proposed method.