Adaptive control optimization in end milling using neural networks

In this paper, we propose an architecture with two different kinds of neural networks for on-line determination of optimal cutting conditions. A back-propagation network with three inputs and four outputs is used to model the cutting process. A second network, which parallelizes the augmented Lagrange multiplier algorithm, determines the corresponding optimal cutting parameters by maximizing the material removal rate according to appropriate operating constraints. Due to its parallelism, this architecture can greatly reduce processing time and make real-time control possible. Numerical simulations and a series of experiments are conducted on end milling to confirm the feasibility of this architecture.