Startseite A dynamic pressure strategy to minimize void formation in vacuum infusion
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A dynamic pressure strategy to minimize void formation in vacuum infusion

  • Chih-Yuan Chang EMAIL logo
Veröffentlicht/Copyright: 10. Januar 2025
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Abstract

An improved vacuum infusion (VI) process, called dynamic pressure molding (DPM), has been proposed to reduce void formation during infusion. In this method, the conventional VARTM process is modified by mounting a rigid chamber on top of the mold. The compaction pressure on the vacuum bag and the infusion pressure are independently and dynamically controlled during infusion to achieve the optimal impregnation for avoiding void formation. To reduce the postinfusion time, the premature sealing of the inlet, the heated air compression, and the two-sided drainage are also used in the DPM. Two different characteristics of the preform are tested at various infusion strategies. A one-dimensional nonisothermal filling model coupled with preform deformation is performed to numerically analyze the complete filling process. Results show that the four-step DPM enables at least 96.53 % of the infusion phase to be within the optimal infusion mode, but its effectiveness in reducing the total filling time depends on the preform characteristics. Several limitations of the DPM are interpreted. For comparison purposes, the typical VI process is also modeled.


Corresponding author: Chih-Yuan Chang, Department of Mechanical and Automation Engineering, 34907 Taiwan Steel University of Science and Technology , Lu-Chu District, 821013, Kaohsiung, Taiwan, E-mail:

Funding source: Ministry of Science and Technology of Republic of China

Award Identifier / Grant number: MOST 108-2221-E−244 -001

  1. Research ethics: The local Institutional Review Board deemed the study exempt from review.

  2. Informed consent: Not applicable.

  3. Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: To edit grammar and improve language for a few paragraphs.

  5. Conflict of interest: The author states no conflict of interest.

  6. Research funding: This work was supported by the Ministry of Science and Technology of Republic of China under Grant [MOST 108-2221-E-244-001].

  7. Data availability: All data are provided in full in the results section of this paper.

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Received: 2024-05-02
Accepted: 2024-12-08
Published Online: 2025-01-10
Published in Print: 2025-03-26

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