Analysis and compensation of kinematic errors in a five-axis machine tool based on an internal data-driven method

Abstract The increasing demand for highly efficient and precise machining in the aerospace industry is leading to increasingly stringent requirements for machine tool accuracy measurement and compensation technologies. However, existing methods can only measure the accuracy of machine tools after they have been shut down, using instruments that do not measure the kinematic errors during the machining process. To address this issue, a method for real-time prediction of kinematic errors in a five-axis machine tool has been proposed to enhance machining accuracy and efficiency. The method employs a neural network-based predictive model, which has been trained and validated through experimental testing and data collection using internal sensors of the machine tool. The study reveals that this predictive model can effectively forecast the kinematic errors, through real-time compensation, and can reduce errors by 60-86%, significantly enhancing machine performance.