journal article·2019-05

Computational modeling for cure process of carbon epoxy composite block

Jin-Sang Yoon, Kibum Kim, Hyoung-Seock Seo

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Abstract

Abstract Serious defect often occurs inside the carbon fiber reinforced plastics (CFRP) block during the cure process due to the inappropriate curing cycle. This paper presents a computational approach proposed to describe the cure process of carbon epoxy composite block based on Finite Difference Method (FDM). The three dimensional FDM model developed was capable of calculating complicated inhomogeneous anisotropic matrix and two phase phenomena of the resin curing in transient state. The model was used to determine the temperature distribution and the cure degree of the CFRP block. The simulation was carried out for blocks in two different sizes cured for the autoclaving process, and simulation results were validated with the C-scan experimental data. In addition, the optimum cure cycle was proposed; therefore, the temperature different in the block, especially between the perimeter and the core regions could be minimized, and simultaneously the input energy consumption could be reduced during the entire cure cycle. It was found that the energy consumption could be reduced by increasing the heat suppling rate before the block temperature reached the polymerization point of the resin and setting the maximum temperature in short period without increasing the temperature deviation between the core and surface of the block. The total energy saving was ranging from 7 to 35% in maximum depending on the method and dimensions. This study provided a reliable way to assess any possible defect in composite block generating during curing process, and the more effective cure cycle could be customized for any specific dimension and cure method.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Epoxy Resin Curing Processes · Additive Manufacturing and 3D Printing Technologies · Photopolymerization techniques and applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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