Neeraj Rai

Research Project

Cure Cycle Optimization for the Fabrication of Thermosetting-Matrix Composites

Polymer-matrix composites using thermosetting resins as the matrix are increasingly finding use in several applications. However, a major impediment to their widespread commercial use is the high cost associated with their manufacture, arising from the long processing cycle times. This study addresses the problem of determining cure temperature and pressure variations with time (referred to as the cure cycles) for a time-optimal fabrication of thermosetting-matrix composites subject to practical constraints. The optimal cure cycles are determined using a nonlinear programming scheme combined with experimentally-validated numerical models to simulate the physical process phenomena.

The numerical process models used in the above-mentioned approach are often computationally intensive, and the computational burden is further increased as a result of the iterative invocation of the models during the optimization process. Towards alleviating the computational tedium, the study also presents the use of artificial neural networks trained using the physical process models as a practical substitute for rigorous numerical simulations in the process optimization endeavor in particular, and simulation-assisted materials manufacturing, in general.

Publications

1. N. Rai and R. Pitchumani, "Application of Neural Networks to Optimization of Cure Cycles for the Manufacture of Composites," ASME Paper 96-WA/AMD-9, 1996.
2. N. Rai and R. Pitchumani, "Optimal Cure Cycles for the Fabrication of Thermosetting-matrix Composites," Polymer Composites, 18(4), 566-581, 1997.
3. N. Rai and R. Pitchumani, "Rapid Cure Simulation Using Artificial Neural Networks," Composites Part A: Applied Science and Manufacturing, 28(9-10), 847-859, 1997.
4. N. Rai and R. Pitchumani, "Neural Network-based Optimal Curing of Composite Materials," Journal of Materials Processing and Manufacturing Science, 6(1), 39-62, 1997.