Abstract: The machining accuracy of five-axis CNC machine tools is usually measured by contour error. The traditional contour error reduction strategies mainly include accurate contour error estimation and effective contouring controller design. However, there are problems in traditional strategies, such as online contour error estimation or complex controller design. To this end, based on the mapping between the input drive commands of machine tool and the output pose, a data-driven contour error compensation strategy is firstly proposed for five-axis CNC machine tools. First, a PID controller is adjusted to ensure the stable tracking of single axis, and the input commands and actual output pose of machine tool are collected at the same time. Then, according to the tool pose and orientation of five-axis CNC machine tool, dual deep neural network models for the tool pose and orientation are built respectively, and the new reference inputs can be predicted based on the neural network models obtained from training data. Finally, a five-axis tool path is used to carry out the experiments. The experimental results show that the proposed contour error compensation strategy does not require the online contour error estimation and effective controller design, which can reduce the position and orientation contour errors effectively.

Key words: contour error, contouring control, deep neural network, reference input commands