Application of a neural network to improve an automated thermoplastic tow-placement process

Abstract

This study demonstrates the use of an on-line neural network to calculate process set points for PID controllers in a manufacturing process such as the automated thermoplastic tow-placement (ATP) technique. The set points are computed by the neural network so that the throughput is near maximum and a desired minimum quality is maintained. A novel neural network predictive scheme is developed to enable performance over a wide range of processing inputs. Process history can greatly affect the final part quality and, therefore, is an integral part of the method for determining the set points. The system is first trained and tested in simulation and then validated for the highly non-linear ATP process resulting in significantly improved process operation. The developed approach is applicable to many other manufacturing processes where process simulations exist and conventional control techniques are lacking.