Parameter Optimization for Vibration Flow Field of Polystyrene Melts via RBF Neural Networks Combined with SALS

Abstract:

This work aims at developing an accurate measurement of characterization flow field of polymer melts by small-angle light scattering (SALS). In this article we propose a new method, based on radial basis function neural network (RBFNN) for predicting the optimum vibration field parameters. A laser light passes through polymer melts in the visual slit die. The results reported in this study were obtained with polystyrene (PS) with rotation speed at 20 rpm. In order to capture the scattered light, a polarizer and an analyzer are placed before and after the polymer melts. RBFNN inputs consist of frequency and amplitude, which are used as input parameters to predict the maximum light intensity projection area. RBFNN predicts that the optimum value of frequency, amplitude are 15.86 Hz and 0.20mm, respectively. And the maximum light intensity projection area is predicted to be 9260 pixels.

• Periodical: Advanced Materials Research (Volumes 602-604)
• Pages: 757-761
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.602-604.757
• Online since: December 2012
• Authors: Guang Ming Xian, Jing Ping Qu, Bi Qing Zeng
• Keywords: Parameter Optimization, Polysrtyrene Melt, RBF Neural Network, Small Angle Light Scattering

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