Optimization of a centrifugal electrospinning process using response surface methods and artificial neural networks - Greenawalt, Frank E., author; Duff, William S., advisor; Bradley, Thomas H., committee member; Labadie, John W., committee member; Popat, Ketul C., committee member (Colorado State University. Libraries, 2014)
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 of these three approaches is made in conjunction with experimentation on a newly developed centrifugal electrospinning prototype. The centrifugal electrospinning process is taken from the exploratory design phase through the pre-production phase to determine optimized manufacturing operating conditions. Centrifugal Electrospinning is a sub platform technology to electrospinning for producing nanofibrous materials with a high surface to volume ratio, significant fiber interconnectivity and microscale interstitial spaces. [131] Centrifugal electrospinning is a potentially more cost effective advanced technology which evolved from traditional electrospinning. Despite there being a substantial amount of research in centrifugal electrospinning, there are still many aspects of this complex process that are not well understood. This study started with researching and developing a functional centrifugal electrospinning prototype test apparatus which, through patent searches, was found to be innovative in nature. Once a functional test apparatus was designed, an exploration of the process parameter settings was conducted to locate an experimental setup condition where the process was able to produce acceptable sub-micron polymeric fibers. At this point, the traditional RSM/DOE approach was used to find a setting point that produced a media efficiency value that was close to optimal. An Artificial Neural Network architecture was then developed with the goal of building a model that accurately predicts response surface values. The ANN model was then used to predict responses in place of experimentation on the prototype in the RSM/DOE optimization process. Different levels of use of the ANN were then formulated using the RSM/DOE and ANN to investigate its potential advantages in terms of time, and cost effectiveness to the overall optimization approach. The development of an innovative centrifugal electrospinning process was successful. A new electrospinning design was developed from the research. A patent application is currently pending on the centrifugal electrospinning applicator developed from this research. Near optimum operating settings for the prototype were found. Typically there is a substantial expense associated with evolving a well-designed prototype and experimentally investigating a new process. The use of ANN with RSM/DOE in the research was seen to reduce this expense while identifying settings close to those found when using RSM/DOE with experimentation alone. This research also provides insights into the effectiveness of the RSM/DOE approach in the context of prototype development and provides insights into how different combinations of RSM/DOE and ANN may be applied to complex processes.
Keywords: ANN; RSM