Identification and optimization of CNC dynamics in time-dependent machining processes and its validation to fluid jet polishing

In time dependent CNC processes such as polishing, a target material removal profile is obtained by creating and inputting G-codes with varying feed into the controller of a machine tool. The input G-codes are firstly filtered by a CNC interpolator, and the real-time generated signal then goes to a feed drive system of the machine. Through these steps, the original signal is distorted and the ideal feed profile cannot be achieved in some areas. This distortion affects the material removal distribution, and thus the accuracy of the final polished surface profile. While a number of methods have been developed for predicting polished surfaces, most of them fail to consider the influence of controller dynamics. In this paper, a method for accurate prediction of polished surfaces is proposed that considers the error contribution from control signal distortion, and a strategy for compensation of this error. Firstly, the principles of identification of control system behavior in a machine tool are explained, and a process model for Fluid Jet Polishing (FJP) is introduced. Secondly, the transfer functions of the control system are identified, and the process model calibrated against experimental data. Thirdly, a simulator for predicting polished surface profiles is implemented that combines the control system and process model. A micro-groove polishing experiment is carried out that validates the reliability of the proposed method, and shows its limitations. Finally, an algorithm based on particle swarm optimization is proposed for reducing the impact of controller dynamics in micro-groove polishing.