Monocular-vision-based contouring error detection and compensation for CNC machine tools

Contouring error detection for machine tools can be used to effectively evaluate their dynamic performances. For the state-of-the-art non-vision measurement devices, they have limitations on arbitrary and high cross-scale contouring error measurement (e.g., ball-bar and cross-grid encoder). Besides, error motions of irrelevant axes are inevitably introduced in the measurement process (e.g., ball-bar, R-test). The vision-based method provides a promising way to address the problems. In this paper, to further the study, a cost-effective monocular-vision-based two-dimensional arbitrary contouring error detection method is proposed. Compared to the existing vision-based method, the main progress is to use the idea of error distribution to simultaneously extend the measurable working range and traverse speed. The basic idea of the proposed method is to improve the measurable traverse speed by sacrificing field of view (FOV), and to deduce the wide range contouring error using priori information. Both experiments for the detection and compensation of contouring error are performed in a CNC machine tool. The contouring error detection results, in contrast to the cross-grid encoder, show that the average detecting error at feed rate of 5000 mm/min is about 4 μm, which verifies measurement accuracy and feasibility of the proposed method. Besides, the compensation results show that the maximum and average contouring error measured after compensation at 5000 mm/min decrease by 66.39% and 56.47%, respectively, which validate the potential of the proposed method in effectively improving machine tool's dynamic performance.