These days, the interests on the high speed handling robots are increasing because it is important to get down the unit cost of production to get the price competitiveness. The SCARA robot with simple mechanism is more suitable to implement the high speed robot system as well known. The moving parts of SCARA robot have to be designed for high speed. But the structural analysis is induced by the robot links because they drive in high acceleration and deceleration. In this reason, the structural analysis of the high speed SCARA robot is very important in the design process. In this paper, the study on the structural analysis of a high speed SCARA robot has been done and the research results will be introduced.

This study proposes the compensation method for the mechanical deflection error of a SCARA robot. While most studies on the related subject have dealt with the development of a control algorithm for improvement of robot accuracy, this study presents the control method reflecting the mechanical deflection error which is predicted in advance. The deflection at the end of the gripper of SCARA robot is caused by the self-weights and payloads of Arm 1, Arm 2 and quill. If the deflection is constant even though robot’ posture and payload vary, there may not be a big problem on robot accuracy because repetitive accuracy, that is relative accuracy, is more important than absolute accuracy in robot. The deflection in the end of the gripper varies as robot’ posture and payload change. That’ why the moments ,  and  working on every joint of a robot vary with robot’ posture and payload size. This study suggests the compensation method which predicts the deflection in advance with the variations in robot’ posture and payload using neural network. To do this, I chose the posture of robot and the payloads at random, found the deflections by the FEM analysis, and then on the basis of this data, made compensation possible by predicting deflections in advance successively with the variations in robot’ posture and payload through neural network learning.

In this paper, optimal design of the second arm in a SCARA robot was studied. The mass and moment of inertia of the second arm of a SCARA robot have great effects on performance indices such as cycle times and torques of the first and second axes. To reduce the mass and moment of inertia, optimal design was carried out by FEM analysis using parameters such as width and height of the arm rib, which was newly adopted to decrease the arm thickness in keeping stiffness. Computer simulation was conducted in X and Y directional paths. As a result of the optimal design, maximum torques of the first and second axes decreased by 10.1% in maximum.