Online estimation and monitoring of local permeability in resin transfer molding
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- Bai‐Jian Wei
- Department of Chemical Engineering National Tsing Hua University Hsinchu 30013 Taiwan Republic of China
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- Yu‐Sung Chang
- Department of Chemical Engineering National Tsing Hua University Hsinchu 30013 Taiwan Republic of China
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- Yuan Yao
- Department of Chemical Engineering National Tsing Hua University Hsinchu 30013 Taiwan Republic of China
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- Jun Fang
- Analytical and Systems Research, Arkema Inc. King of Prussia Pennsylvania 19406
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<jats:p>Resin transfer molding (RTM) is a popular manufacturing method of composite materials, which have been widely used in many areas including aeronautic, automotive industries, etc. In RTM, permeability of fiber reinforcement varies with its geometric formation and affects the property of resin flow, which influences the final product quality. Therefore, effective estimation of permeability is crucial to achieving good process control and satisfactory quality product. In this article, a method of online estimating and monitoring local permeability is proposed. It can deal with variation in local permeability within preform caused by irregular arrangement of fibers among different regions. This study is divided into three stages. In the first stage, flow visualization was realized and all hardware was integrated to acquire real‐time information in resin filling period. In the second stage, local pressure and flow front location were substituted into the Darcy's law, thus making online calculation of local permeability feasible. Then, in the third stage, the statistical process control charting technique was adopted to identify the changes in permeability. The proposed methods were used in trial RTM tests to compare their results to those from the reference method and to confirm their effectiveness. POLYM. COMPOS. 37:1249–1258, 2016. © 2014 Society of Plastics Engineers</jats:p>
収録刊行物
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- Polymer Composites
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Polymer Composites 37 (4), 1249-1258, 2014-11-03
Wiley
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詳細情報 詳細情報について
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- CRID
- 1362544420327253888
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- DOI
- 10.1002/pc.23290
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- ISSN
- 15480569
- 02728397
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- データソース種別
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- Crossref