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Polymers 2016, 8(9), 337; doi:10.3390/polym8090337

Model-Assisted Control of Flow Front in Resin Transfer Molding Based on Real-Time Estimation of Permeability/Porosity Ratio

Department of Chemical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
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Academic Editor: Yingjie Xu
Received: 27 June 2016 / Revised: 29 August 2016 / Accepted: 5 September 2016 / Published: 8 September 2016
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Abstract

Resin transfer molding (RTM) is a popular manufacturing technique that produces fiber reinforced polymer (FRP) composites. In this paper, a model-assisted flow front control system is developed based on real-time estimation of permeability/porosity ratio using the information acquired by a visualization system. In the proposed control system, a radial basis function (RBF) network meta-model is utilized to predict the position of the future flow front by inputting the injection pressure, the current position of flow front, and the estimated ratio. By conducting optimization based on the meta-model, the value of injection pressure to be implemented at each step is obtained. Moreover, a cascade control structure is established to further improve the control performance. Experiments show that the developed system successfully enhances the performance of flow front control in RTM. Especially, the cascade structure makes the control system robust to model mismatch. View Full-Text
Keywords: polymer composite; resin transfer molding; flow control; permeability estimation; meta-model; cascade control polymer composite; resin transfer molding; flow control; permeability estimation; meta-model; cascade control
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Wei, B.-J.; Chuang, Y.-C.; Wang, K.-H.; Yao, Y. Model-Assisted Control of Flow Front in Resin Transfer Molding Based on Real-Time Estimation of Permeability/Porosity Ratio. Polymers 2016, 8, 337.

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