Optimising Parameters of Fused Filament Fabrication Process to Achieve Optimum Tensile Strength Using Artificial Neural Network - Collections | Kyushu University Library

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＜journal article＞
Optimising Parameters of Fused Filament Fabrication Process to Achieve Optimum Tensile Strength Using Artificial Neural Network

Creator	Creator Name Weake, Nawaharsh Affiliation Affiliation Name Department of Mechanical Engineering, National Institute of Technology Delhi
	Creator Name Pant, Meena Affiliation Affiliation Name Department of Mechanical Engineering, National Institute of Technology Delhi
	Creator Name Sheoran, Ankita Affiliation Affiliation Name Department of Mechanical Engineering, National Institute of Technology Delhi
	Creator Name Haleem, Abid Affiliation Affiliation Name Faculty of Engineering and Technology, Jamia Millia Islamia
Language	English
Publisher	Transdisciplinary Research and Education Center for Green Technologies, Kyushu University
Publisher	九州大学グリーンテクノロジー研究教育センター
Date	2020-09
Source Title	Evergreen
Vol	7
Issue	3
First Page	373
Last Page	381
Publication Type	Version of Record
Access Rights	open access
Crossref DOI	https://doi.org/10.5109/4068614
Rights	Creative Commons Attribution-NonCommercial 4.0 International
Abstract	Currently, the Additive Manufacturing (AM) process has become the most researched area, leading to a revolution in the manufacturing industries. Among all additive manufacturing techniques, fused fila...ment fabrication (FFF) is famous because simple to use and economical. However, in the FFF process, the final quality of parts depends upon the rigorous selection of process parameters, as it is necessary to understand the physical phenomena of process variables and their impact on the mechanical properties. This study involves the independent analysis of three process variables like layer thickness, orientation, and printing temperature. Taguchi L9 orthogonal array opted to investigate the tensile strength of Acrylonitrile butadiene styrene (ABS). By using this, the number of experimental reduced from L27 to L9 experiments. The specimens are fabricated based on ASTM D-638 tensile standard design. For training and testing purposes, the Artificial Neural Network tool has opted by using MATLAB software 2015.show more

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Pages_373-381	pdf	1.28 MB	789

Details

PISSN	2189-0420
EISSN	2432-5953
Record ID	4068614
Peer-Reviewed	Refereed
Subject Terms	Additive manufacturing
	Fused filament fabrication
	3-D printer
	Artificial neural network
Created Date	2020.10.02
Modified Date	2024.02.21