The error prediction and pre-compensation methods for multi-axis CNC machine tools have long been a research focus in the industrial manufacturing field. Despite advancements, challenges persist, including high computational costs and poor prediction accuracy. Therefore, effective error prediction and pre-compensation methods are crucial for cost reduction and efficiency improvement. This paper proposes an error prediction and pre-compensation method, demonstrated through a six-axis machining example. First, a model based on the GRU neural network technology is proposed to predict and optimize laser focal position and directional profile errors. The method was applied in automotive chassis tubing cutting experiments to assess its feasibility and precision in practical cutting processes. The maximum absolute values of the laser focal position and directional profile errors were reduced by 51.32% and 62.50%, respectively, while the average absolute errors were reduced by 59.35% and 69.64%, verifying the correctness and accuracy of the process optimization method. To address the high computational demands, we propose a digital twin-driven GRU-based approach. This methodology establishes dynamic interactions between virtual and real processes, facilitating iterative optimization of predictive accuracy and enhancing the level of manufacturing digitalization.
Kang et al. (Tue,) studied this question.