Optimization of a centrifugal electrospinning process using response surface methods and artificial neural networks
For complex system designs involving a large number of process variables, models are typically created for evaluating the system behavior for various operating conditions. These models are useful in understanding the effect that various process variables have on the process response(s). Design of Experiments (DOE) and Response Surface Methodology (RSM) are typically used together as an effective approach to optimize a process. RSM and DOE commonly employ first and second order algebraic models. Artificial Neural Networks (ANN) is a more recently developed modeling approach. An evaluation ...