Research on Data Optimization in Automatic Adjustment of Coordinator Supporting Structure

Abstract

The coordinator of the arc tooth structure is the core component of the seeker product, and the difficulty in the manufacturing process lies in the assembly of the arc gear supporting structure, which needs manual adjustment so that the final friction force can satisfy the design requirement. However, too many motion pairs in the actual operation make the adjustment more complicate and difficult, thus the quality consistency and assembly efficiency are difficult to be guaranteed. Therefore, KUKA iiwa LBR robots were applied to perform automated adjustment of the supporting structure. During the experiment, it was found that the torque data and the end force data were greatly different from the required quality due to the system error and the sensor error. As a result Kalman filtering algorithm and the orthogonal test method were conducted to analyze the data and optimize the parameters aiming at this two types of data. The state prediction equation and other coefficients required by Kalman filtering algorithm were deduced, and the algorithm was embedded in the robot adjustment control program. Result showed that the data fluctuations collected from the robot torque sensor were greatly reduced, and the curve was noticeably smoothed. Meanwhile the orthogonal test of the end force data of the robot was carried out, and the optimal combined parameters were obtained. The variance analysis showed that the importance of three factors was in order of time interval, moving speed and acceleration, where the first two had significant differences in the items. Final test was arranged to verify the combined parameters, and the standard deviation was among the requirements. The optimized data and process parameters were also verified in the later overall system adjustment test, and the adjustment effect and efficiency have been improved.