Optimization of autoclave processing of polymer composites using a genetic algorithm

Current industrial autoclave cure cycles used to process composites in the aerospace industry are conservative and costly. It is therefore desirable to optimize cure cycles based on industrial quality requirements to reduce costs. Process optimization requires several key components including a process model, an optimizing scheme, and an objective function. To address this problem, the University of Manitoba and its industrial partners have set out to develop the Advanced Process Control System (APCS). As a portion of this project, the primary objective of this thesis was to develop an objective function and interface with a genetic algorithm based path generator and a process model COMPRO, developed at The University of British Columbia, to develop the optimizer for autoclave process cycle optimization. Models run by the process model used Cytec-Fiberite 934 with Toray T300 fibres. As a secondary objective for the thesis, material characterization was performed for Hexcel F155 resin with Toho T300 carbon fibres for use in future work with APCS. Heat transfer and warpage predictions made by the process model were validated by experiments performed at Boeing Technology Canada - Winnipeg Division. This thesis discusses the effects of various versions of the objective function and the inputs required by the genetic algorithm. Based on the optimization results for various optimizer settings used in the study, the thesis concludes by providing the best optimizer configuration.