Development of Pitch Cycle-Based Iterative Learning Contour Control for Thread Milling Operations in CNC Machine Tools

Abstract

The helical contour motion accuracy of feed drive axes is important for thread milling operations in computer numerical control (CNC) machine tools. However, the motion dynamics and external disturbances significantly affect the contour motion results, while the feed drive axes perform helical motions in thread milling operations. Although existing iterative learning contour control (ILCC) methods can improve contour motion accuracy, the problems of data recording and processing on memory usage and computational burden in control systems, wasted materials, and increased costs in thread manufacturing still limit the practical applications of ILCC. Therefore, considering the similar motion dynamics and external disturbances of the feed drive axes during the pitch cycle motions of a helical path, this study developed a pitch cycle-based iterative learning contour control (PCB-ILCC) method to address the control system and thread manufacturing problems caused by the use of ILCC. For PCB-ILCC, this study adopted contour error vector estimation by referring to the interpolated positions on the pitch cycle of the helical path to simplify the computational complexity and designed the ILCC using the cycle learning method to easily implement the ILCC structure. Thus, this study developed a permanent magnet synchronous motor (PMSM) driving control utilizing the robust control method to mitigate the problems of motion dynamics and external disturbances on the feed drive axes. Thread milling experiments performed on a five-axis CNC machining center demonstrated the feasibility of the PCB-ILCC and validated that it can significantly improve the helical contour motion accuracy of the feed drive axes and achieve an 80% contour error reduction rate in comparison with the proportional–proportional–integral control, which is extensively used in commercialized PMSM drivers and CNC controllers.