Combined feedforward and feedback control of end milling system

Purpose: Purpose of this paper. An intelligent control system is presented that uses a combination of feedforward and feedback for cutting force control in end milling. Design/methodology/approach: The network is trained by the feedback output that is minimized during training and most control action for disturbance rejection is finally performed by the rapid feedforward action of the network. Findings: The feedback controller corrects for errors caused by external disturbances. The feedforward controller is an artificial neural network (ANN) which approximates the inverse dynamics of the machining process. Research limitations/implications: The dynamic architecture of the neural controller is chosen, and the methods for delay time treatment and training network on line are investigated. The controller was designed and tested using a simulator model of the milling process that includes feed drive model and cutting dynamics simulator. Practical implications: An application to cutting force control in end-milling is used to prove the effectiveness of the control scheme and the experiments shows that the dynamic performance of the cutting force control is greatly improved by this neural combined control system. Originality/value: New combined feedforward and feedback control system of end milling system is developed and tested by many experiments. Also a comprehensive user-friendly software package has been developed to monitor the optimal cutting parameters during machining.