Dynamic feedrate optimization for parametric toolpath with data-based tracking error prediction

Abstract

Abstract As parametric toolpath has advantages in improving machining efficiency and machining precision simultaneously, it has become increasingly widespread in Computer Numerical Control (CNC) systems and the planning performance for smooth toolpath become crucial in machining process. The most important goal for practical machining is to ensure machining accuracy (i. e. limited tracking error) with the highest machining efficiency. However, machining accuracy and efficiency are two conflicting indicators and limited tracking error is always obtained by setting lower kinematic constraints in traditional methods, which will reduce machining efficiency in return. This is mainly due to that only kinematic performances are considered while dynamic analysis is ignored. In order to establish the relation between machining accuracy and efficiency, a data-based method is presented for a feedback dynamic PID controller in this paper, and the upper bound of the tracking error is predicted with input sampling points. With the dynamic performance and tracking error of all axes, a feedrate optimization method is introduced to obtain the fastest machining efficiency afterwards. Furthermore, since the dynamic and tracking error performance are not linear constraints, the other contributions of this paper are to linearize the limitations and obtain a near-optimal feedrate profile efficiently. Finally, experiments are designed to validate the proposed method compare to traditional kinematic method. The results show that the proposed method can obtain limited tracking error and the corresponding machining efficiency reduced compared to kinematic method.