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This paper presents using bootstrap aggregated neural networks for the modelling and optimisation control of reactive polymer composite moulding processes. Bootstrap aggregated neural networks combine multiple neural networks developed from bootstrap re-sampling replications of the original training data in order to enhance model prediction and generalisation capability. Neural network models for modelling the degree of cure (through modelling the measured resistance) are developed from real industrial process operational data. Both static and dynamic models are developed and the developed neural network models are validated on unseen process operation data. The bootstrap aggregated neural network models give accurate and reliable predictions than single neural networks. Optimal heating profile is obtained by solving an optimisation problem using the dynamic neural network model. The model prediction confidence bound is incorporated in the optimisation objective function in order to enhance the reliability of the calculated optimal control profile. In addition to maximise the final degree of cure, model prediction confidence bound is minimised. Application results on a simulated polymer composite moulding process demonstrate that the proposed reliable optimisation control strategy is effective.

RELIABLE MODELLING AND OPTIMISATION CONTROL OF REACTIVE POLYMER COMPOSITE MOULDING PROCESSES USING BOOTSTRAP AGGREGATED NEURAL NETWORK MODELS