Monitoring of fused filament fabrication (FFF) : An infrared imaging and machine learning approach

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00008305
URN to cite this document: urn:nbn:de:bvb:703-epub-8305-7

Title data

Bauriedel, Niklas ; Albuquerque, Rodrigo Q. ; Utz, Julia ; Geis, Nico ; Ruckdäschel, Holger:
Monitoring of fused filament fabrication (FFF) : An infrared imaging and machine learning approach.
In: Journal of Polymer Science. Vol. 62 (2024) Issue 24 . - pp. 5633-5641.
ISSN 2642-4169
DOI der Verlagsversion: https://doi.org/10.1002/pol.20240586

	Format: PDF Name: Journal of Polymer Science - 2024 - Bauriedel - Monitoring of fused filament fabrication FFF An infrared imaging and.pdf Version: Published Version Available under License Creative Commons BY 4.0: Attribution
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Abstract

Additive manufacturing holds great promise for broader future use, but quality assurance and component monitoring present notable challenges. This study tackles monitoring Fused Filament Fabrication (FFF) via infrared imaging to forecast the mechanical traits of 3D-printed items. It highlights how temperature variations, influenced by the infill's alternating orientation, affect printed parts' mechanical properties. Utilizing Machine Learning, notably the Random Forest Regressor, this research validates the capability to accurately predict tensile strength from infrared temperature readings, offering a simple, yet effective, real-time FFF monitoring method without specialized hardware. This approach enhances the quality and dependability of 3D-printed components with IR thermal monitoring and machine learning predictions. Highlights Infrared imaging and machine learning are combined to monitor 3D printing. A cost-effective and accessible non-destructive monitoring method is proposed. Temperature variation patterns of 3D printed layers influence mechanical properties.

Further data

Item Type:	Article in a journal
Keywords:	additive manufacturing; fused filament fabrication; IR imaging, machine learning; mechanical properties
DDC Subjects:	600 Technology, medicine, applied sciences > 620 Engineering
Institutions of the University:	Faculties > Faculty of Engineering Science > Chair Polymer Materials > Chair Polymer Materials - Univ.-Prof. Dr.-Ing. Holger Ruckdäschel Faculties Faculties > Faculty of Engineering Science Faculties > Faculty of Engineering Science > Chair Polymer Materials
Language:	English
Originates at UBT:	Yes
URN:	urn:nbn:de:bvb:703-epub-8305-7
Date Deposited:	14 Mar 2025 06:32
Last Modified:	14 Mar 2025 06:33
URI:	https://epub.uni-bayreuth.de/id/eprint/8305

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