This project involves the use of neural networks for adaptive control of manufacturing processes. A methodology is developed to use neural networks in a parallel computing environment. In addition, a parallel recursive least squares algorithm is designed to train recurrent neural networks. Both on-line and off-line training are accomplished. The methodology is validated by applying it to the problem of controlling the cutting forces in a three-dimensional end milling operation using computer simulations and laboratory experiments.*** //
