Startseite Gas assisted fused deposition modeling: effects of assist gas parameters on print quality and properties
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Gas assisted fused deposition modeling: effects of assist gas parameters on print quality and properties

  • Xiaojie Zhang , Xiaoyu Hong , Jianhua Xiao , Mengyu Wang , Jinkuk Kim und Lan Cao ORCID logo EMAIL logo
Veröffentlicht/Copyright: 2. Dezember 2024
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Abstract

Fused deposition modeling (FDM) is the most widespread type of additive manufacturing technology. However, the extrusion based process limits the interfacial bonding strength and dimensional accuracy of the printed parts. This paper presents a gas assisted nozzle to localized heating around filament through hot air flow, to maintain the temperature of the filament stays and improve the quality of the bonding. The impact of assist gas temperature (55 °C–295 °C), flow rate (1 L/min–3 L/min) and pressure (0.2 MPa–0.5 MPa) on filament extrudation, layer consolidation, and the printed parts thermal properties, as well as the mechanical properties were investigated. It is shown that the swell ratio of extruded filament and dimensional difference of layer thickness can be controlled by varying the assist gas parameters. The assist gas raises the temperature of the exudate and the existing layer near the nozzle, leading to 73.6 % increase in crystallinity, 19.4 % increase in tensile strength and 48.4 % impact strength. The gas assisted pre-heating approach represents an effective way to increase interlayer strength can be employed as an additional control parameter to improve the thermal and mechanical properties of the FDM printed parts.


Corresponding author: Lan Cao, School of Polymer Science and Engineering, Qingdao University of Science and Technology, Zhengzhou Road, Qingdao, Shandong 266042, China, E-mail:

Award Identifier / Grant number: 52063021

Award Identifier / Grant number: EA202001381

Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 52403096

Funding source: Taishan Scholars Program

Award Identifier / Grant number: TSQN202312206

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: Xiaojie Zhang: methodology, validation, manuscript writing. Xiaoyu Hong: experimentation, original draft preparation. Jianhua Xiao: supervision, conceptualisation. Mengyu Wang: data collection. Jinkuk Kim: supervision, conceptualisation. Lan Cao: manuscript reviewing, interpretation, manuscript reviewing and editing. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: This work was supported by the PhD Research Startup Foundation of Nanchang Hangkong University [grant no. EA202001381]; the National Natural Science Foundation of China (NSFC) [grant no. 52063021]; the National Natural Science Foundation of China [grant no. 52403096]; and the Taishan Scholars Program [grant no. TSQN202312206].

  7. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2024-06-15
Accepted: 2024-09-29
Published Online: 2024-12-02
Published in Print: 2025-01-29

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Heruntergeladen am 14.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/polyeng-2024-0126/html
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