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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM15466
Volume:
6 |
7 |
Jul 2022
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
In this project we have focused on the analysis of the mechanical properties of thin-walled specimens fabricated by fused deposition modeling (FDM). Two materials were considered, polylactide (PLA) and Nylon. The study describes how the specimens with different thicknesses and printing orientations were designed, printed, and subjected to testing. Four potential factors are considered namely layer height, Temperature, shell, infill density, where the speed is constant while s/n ratio and hardness of the specimens is considered as the output. In Fused Deposition Modeling (FDM) three-dimensional (3D) printing the printed part is greatly affected by the process parameters, therefore the parameters must be selected properly to enhance the characteristics of the final product. In view of this, the present project experimentally and statistically studied the effect of various printing parameters namely extruder temperature, infill density, shell number, and layer height on tensile strength using Polylactic acid (PLA) filament and Nylon. Based on Taguchi’s mixed model fractional factorial design, eighteen experiments were set, and the specimens of PLA and nylon are printed on an FDM 3D printer and tested for tensile strength using the universal testing machine. Thereafter, the optimal combination of the parameters was selected using Signal-to-Noise ratio (S/N), and Rockwell hardness is used for indicating the significant parameters and their effect on tensile strength. Moreover, a linear regression model has been developed to predict the tensile strength of the printed part. Lastly, the confirmation test must be shown that there is a good agreement between the experimental and statistical data.
KEYWORDS
Publication Date
10/6/2022
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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