Prediction of tensile strength in fused deposition modeling process using artificial neural network and fuzzy logic-Reference-Cited by-同舟云学术

Prediction of tensile strength in fused deposition modeling process using artificial neural network and fuzzy logic

Published:2022-09-23 Issue: Volume: Page:
ISSN:2363-9512
Container-title:Progress in Additive Manufacturing
language:en
Short-container-title:Prog Addit Manuf

Author:

Tura Amanuel Diriba^ORCID,Lemu Hirpa G.^ORCID,Mamo Hana Beyene^ORCID,Santhosh A. Johnson^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering

Link

https://link.springer.com/content/pdf/10.1007/s40964-022-00346-y.pdf

Reference27 articles.

1. Gebisa AW, Lemu HG (2018) Investigating effects of Fused-deposition modeling (FDM) processing parameters on flexural properties of ULTEM 9085 using designed experiment. Materials (Basel) 11:1–23

2. Peterson AM (2019) Review of acrylonitrile butadiene styrene in fused filament fabrication: a plastics engineering-focused perspective. Addit Manuf 27:363–371

3. Tura AD, Mamo HB, Jelila YD, Lemu HG (2021) Experimental investigation and ANN prediction for part quality improvement of fused deposition modeling parts. IOP Conf Ser Mater Sci Eng. https://doi.org/10.1088/1757-899X/1201/1/012031/meta

4. Motaparti KP, Taylor G, Leu MC, Chandrashekhara K, Castle J, Matlack M (2017) Experimental investigation of effects of build parameters on flexural properties in fused deposition modelling parts. Virtual Phys Prototyp 2017(12):1–14

5. Dong G, Wijaya G, Tang Y, Zhao YF (2018) Optimizing process parameters of fused deposition modeling by Taguchi method for the fabrication of lattice structures. Addit Manuf 19:62–72

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