Optimization of working position and posture of a 5-DOF hybrid automatic drilling system based on an improved GA-BP neural network

Abstract

ABSTRACT After compensation for positioning error, such errors become different in multipose space. To reduce the positioning error of a 5-degree-of-freedom automatic drilling system, an optimization algorithm is used to optimize the drilling position and posture. The forwards kinematics of the automatic drilling system were determined by a vector cross product. Combined with structural deformation calculation, the workspace of the drilling system was analysed. The pose errors in multipose space were calculated. A BP neural network was used to establish the mapping between the target pose and the pose error after error compensation, and the improved genetic algorithm was used to optimize the working pose. To verify the robustness of the proposed method, drilling comparison experiments were carried out. After optimization, the maximum position and posture error of the parallel posture alignment mechanism were reduced by 71.11% and 67.57%, respectively. The maximum position and normal error of the automatic drilling system were reduced by 54.37% and 19.64%, respectively. Drilling experiments show that the proposed algorithm can meet the accuracy requirements of aircraft assembly fields. The algorithm can be used for working position and posture optimization of a mechanism equipped with posture alignment and an end effector module.