Five-axis contouring error compensation based on variable-gain iterative learning

Complex surface parts applied in aerospace and defense fields require high precision machining standards. The precision of the components produced is heavily influenced by the contouring accuracy of the five-axis computer numerical control (CNC) machine tool. To address this issue, this paper presents a five-axis contouring error compensation scheme based on variable gain iterative learning. Firstly, estimating the contouring error by utilizing the Newton-Raphson method. Secondly, the proposed iterative learning-based contouring error control scheme compensates the estimated contouring error to each motion axis. The regions with large contouring errors on the machining surface can be recognized by the search algorithm, which needs further adjustment utilizing the variable gain parameters strategy. Finally, the method is simulated and verified. Simulation results illustrate that, in comparison with the fixed gain contour control, the proposed method effectively reduces overall contouring errors and enhances machining accuracy in five-axis milling.