Reliable optimisation control of a reactive polymer composite moulding process using ant colony optimisation and bootstrap aggregated neural networks

This paper presents a study on the optimisation control of a reactive polymer composite moulding process using ant colony optimisation and bootstrap aggregated neural networks. In order to overcome the difficulties in developing accurate mechanistic models for reactive polymer composite moulding processes, neural network models are developed from process operation data. Bootstrap aggregated neural networks are used to enhance model prediction accuracy and reliability. Ant colony optimisation is able to cope with optimisation problems with multiple local optima and is able to find the global optimum. Ant colony optimisation is used in this study to find the optimal curing temperature profile. In order to enhance the reliability of the optimisation control policy, model prediction confidence bound offered by bootstrap aggregated neural networks is incorporated in the optimisation objective function so that unreliable predictions are penalised. The proposed method is tested on a simulated reactive polymer composite moulding process.