Performance Prediction and Reverse Design of a Glass Composites Pultrusion Process Based on RBF

DOI

10.2991/wartia-16.2016.287How to use a DOI?

Keywords

Glass Composites, Curing Degree, Mould Temperature, Performance Forecast

Abstract

Most glass composites products in engineering are manufactured by a pultrusion process. Because the pultrusion process is affected by many factors, it is not easy to control the performance of products. In order to obtain good performance and meet the requirements of glass composites products in specific parameters, artificial neural network technology was adopted to optimize and design the pultrusion process. The results showed that the curing degree of products increased gradually with the preheated mould temperature. During the reverse design of glass composites products, it was found that when the spread is 0.3, the measured temperature values of every stage based on curing degree were in good agreement with the forecasting values, which proved the correctness of the reverse design. The research method in this paper provides important theoretical basis and technical support for similar products.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Junming Liu
AU  - Hanwu Liu
AU  - Shuai Luo
AU  - Zhiwen Guo
PY  - 2016/05
DA  - 2016/05
TI  - Performance Prediction and Reverse Design of a Glass Composites Pultrusion Process Based on RBF
BT  - Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications
PB  - Atlantis Press
SP  - 1394
EP  - 1398
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-16.2016.287
DO  - 10.2991/wartia-16.2016.287
ID  - Liu2016/05
ER  -