Optimization in ball-end milling by using adaptive neural controller

In this paper, a neural controller with optimisation for the ball end milling process is described. An architecture with two different kinds of neural networks is proposed, and is used for the on-line optimal control of the milling process. A BP neural network is used to identify the milling state and to learn the appropriate mappings between the input and output variables of the machining process. The feedrate is selected as the optimised variable, and the milling state is estimated by the measured cutting forces. The goal is also to obtain an improvement of the milling process productivity by the use of an automatic regulation of the cutting force. Numerous simulations are conducted to confirm the efficiency of this architecture.