  For manufacturing of aerospace composites, several parts may be processed
simultaneously using convective heating in an autoclave. Due to uncertainties
including tool placement, convective Boundary Conditions (BCs) vary in each
run. As a result, temperature histories in some of the parts may not conform to
process specifications due to under-curing or over-heating. Thermochemical
analysis using Finite Element (FE) simulations are typically conducted prior to
fabrication based on assumed range of BCs. This, however, introduces
unnecessary constraints on the design. To monitor the process, thermocouples
(TCs) are placed under tools near critical locations. The TC data may be used
to back-calculate BCs using trial-and-error FE analysis. However, since the
inverse heat transfer problem is ill-posed, many solutions are obtained for
given TC data. In this study, a novel machine learning (ML) framework is
presented capable of optimizing air temperature cycle in real-time based on TC
data from multiple parts, for active control of manufacturing. The framework
consists of two recurrent Neural Networks (NN) for inverse modeling of the
ill-posed curing problem at the speed of 300 simulations/second, and a
classification NN for multi-objective optimization of the air temperature at
the speed of 35,000 simulations/second. A virtual demonstration of the
framework for process optimization of three composite parts with data from
three TCs is presented.
