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Materiale Plastice
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https://doi.org/10.37358/Mat.Plast.1964

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ISSN Print 0025-5289
ISSN Online 2668-8220
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Materiale Plastice (Mater. Plast.), Year 2020, Volume 57, Issue 3, 202-223

https://doi.org/10.37358/MP.20.3.5394

Ionut Laurentiu Sandu, Florin Susac, Felicia Stan, Catalin Fetecau

Prediction of Polymer Flow Length by Coupling Finite Element Simulation with Artificial Neural Network


Abstract:
In this study, computer-aided engineering (CAE) simulation software and the design of experiments (DOE) method were used to simulate the injection molding process in terms of the melt flow length, using a spiral part. Process parameters such as melt temperature, mold temperature, injection pressure and mold cavity thickness were considered as injection molding variables. A predictive model for the flow length was created using a three-layer artificial neural network (ANN). The ANN model was trained with both simulation and experimental data, and the predictive performances were compared in terms of correlation coefficient, root mean square error and mean relative error. The cavity thickness and melt temperature were found to be the most significant factors for both the simulation and the experiment, while the injection pressure and the mold temperature had little effect on the flow length. The ANN model trained with Moldex3D data shows a significantly higher prediction capacity than the ANN model trained with experimental data. However, the melt flow lengths predicted by the ANN model for both Moldex3D and Moldflow simulation data are statistically significant, indicating that the proposed prediction methodology, which combines the ANN model, DOE method and the CAE simulation technology, can effectively predict the flow length of injection molded parts, with a small number of data.


Keywords:
injection molding; flow length; simulation; Taguchi method; artificial neural network

Issue: 2020 Volume 57, Issue 3
Pages: 202-223
Publication date: 2020/9/30
https://doi.org/10.37358/MP.20.3.5394
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Creative Commons License
This article is published under the Creative Commons Attribution 4.0 International License
Citation Styles
Cite this article as:
SANDU, I.L., SUSAC, F., STAN, F., FETECAU, C., Prediction of Polymer Flow Length by Coupling Finite Element Simulation with Artificial Neural Network, Mater. Plast., 57(3), 2020, 202-223.

Vancouver
Sandu IL, Susac F, Stan F, Fetecau C. Prediction of Polymer Flow Length by Coupling Finite Element Simulation with Artificial Neural Network. Mater. Plast.[internet]. 2020 Jul;57(3):202-223. Available from: https://doi.org/10.37358/MP.20.3.5394


APA 6th edition
Sandu, I.L., Susac, F., Stan, F. & Fetecau, C. (2020). Prediction of Polymer Flow Length by Coupling Finite Element Simulation with Artificial Neural Network. Materiale Plastice, 57(3), 202-223. https://doi.org/10.37358/MP.20.3.5394


Harvard
Sandu, I.L., Susac, F., Stan, F., Fetecau, C. (2020). 'Prediction of Polymer Flow Length by Coupling Finite Element Simulation with Artificial Neural Network', Materiale Plastice, 57(3), pp. 202-223. https://doi.org/10.37358/MP.20.3.5394


IEEE
I.L. Sandu, F. Susac, F. Stan, C. Fetecau, "Prediction of Polymer Flow Length by Coupling Finite Element Simulation with Artificial Neural Network". Materiale Plastice, vol. 57, no. 3, pp. 202-223, 2020. [online]. https://doi.org/10.37358/MP.20.3.5394


Text
Ionut Laurentiu Sandu, Florin Susac, Felicia Stan, Catalin Fetecau,
Prediction of Polymer Flow Length by Coupling Finite Element Simulation with Artificial Neural Network,
Materiale Plastice,
Volume 57, Issue 3,
2020,
Pages 202-223,
ISSN 2668-8220,
https://doi.org/10.37358/MP.20.3.5394.
(https://revmaterialeplastice.ro/Articlegs.asp?ID=5394)
Keywords: injection molding; flow length; simulation; Taguchi method; artificial neural network


RIS
TY - JOUR
T1 - Prediction of Polymer Flow Length by Coupling Finite Element Simulation with Artificial Neural Network
A1 - Sandu, Ionut Laurentiu
A2 - Susac, Florin
A3 - Stan, Felicia
A4 - Fetecau, Catalin
JF - Materiale Plastice
JO - Mater. Plast.
PB - Materiale Plastice SRL
SN - 2668-8220
Y1 - 2020
VL - 57
IS - 3
SP - 202
EP - 223
UR - https://doi.org/10.37358/MP.20.3.5394
KW - injection molding
KW - flow length
KW - simulation
KW - Taguchi method
KW - artificial neural network
ER -


BibTex
@article{MatPlast2020P202,
author = {Sandu Ionut Laurentiu and Susac Florin and Stan Felicia and Fetecau Catalin},
title = {Prediction of Polymer Flow Length by Coupling Finite Element Simulation with Artificial Neural Network},
journal = {Materiale Plastice},
volume = {57},
number = {3},
pages = {202-223},
year = {2020},
issn = {2668-8220},
doi = {https://doi.org/10.37358/MP.20.3.5394},
url = {https://revmaterialeplastice.ro/Articlegs.asp?ID=5394}
}
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