Flow Instabilities in Rheotens Experiments: Analysis of the Impacts of the Process Conditions through Neural Network Modeling

摘要

Fiber spinning experiments are conducted with a capillary rheometer and a Rheotens tester on linear sty-rene-isoprene-styrene copolymer samples by varying extrusion temperature and drawdown velocity in a wide range of values, also covering the occurrence of instability phenomena. Tensile stress is measured during the experiences, and the experimental time series are then analyzed by means of a new methodology. The proposed approach is based on Neural Network modeling of the time series, coupled with Principal Component Analysis postprocessing of the results. The methodology is able to identify and quantify the effects of process condition on the dynamical behavior of the system.