Modeling and optimization of a thermoplastic composites stamping process

Abstract

The potential for short process cycle times is one of the most important characteristics of thermoplastics, leading to rapid, low-cost production of composite structures. One area where thermoplastic composites processing may have the most potential is in high rate stamping. The challenge lies in developing the process to the extent that it exceeds the performance and reliability of its counterpart, steel stamping. To achieve this objective, a better understanding of the process is necessary. In this work, experimental investigations are conducted to determine the effects of composite stamping processing variables on part output which includes tensile strength and modulus. Results of experimentation are used to develop a process model for composite stamping via a supervised learning back propagation neural network.