Optimal Deformation of a Small Plastic Light-guide Using Machine Learning Algorithms

Yoo, Min Ji; Han, Seong Yeol

Min Ji Yoo, Seong Yeol Han

Optimal Deformation of a Small Plastic Light-guide Using Machine Learning Algorithms

Abstract:
Lensknob is a component that transmits light to users. It is essential to minimize the deformation to transmit the light uniformly. As a method of finding injection molding parameters capable of minimizing the deformation, the amount of deformation of the Lensknob was predicted in advance by numerical analysis of the injection molding. However, because it takes a considerable amount of time to analyze, we used the Decision tree as a Machine Learning model. As the injection molding parameters, we set the melting temperature, cooling time, holding time, holding pressure, and ram speed. We set the injection molding parameters based on the range recommended by Moldflow. A full factor method of factor 5 level 3 was applied in the experiment. We predicted the parameters for minimizing the deformation through the Decision tree learned with 243 experimental data. We set the criteria to evaluate the performance of the Decision tree. The parameters predicted by the Decision tree improved the deformation by about 10.37%.

Keywords:
injection molding; CAE; Decision tree; process parameters; deformation; optimization

Issue: 2022 Volume 59, Issue 3

Pages: 128-142

Publication date: 2022/10/3

https://doi.org/10.37358/MP.22.3.5611

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Citation Styles

Cite this article as:
YOO, M.J., HAN, S.Y., Optimal Deformation of a Small Plastic Light-guide Using Machine Learning Algorithms, Mater. Plast., 59(3), 2022, 128-142. https://doi.org/10.37358/MP.22.3.5611

Vancouver
Yoo MJ, Han SY. Optimal Deformation of a Small Plastic Light-guide Using Machine Learning Algorithms. Mater. Plast.[internet]. 2022 Jul;59(3):128-142. Available from: https://doi.org/10.37358/MP.22.3.5611

APA 6th edition
Yoo, M.J., Han, S.Y. (2022). Optimal Deformation of a Small Plastic Light-guide Using Machine Learning Algorithms. Materiale Plastice, 59(3), 128-142. https://doi.org/10.37358/MP.22.3.5611

Harvard
Yoo, M.J., Han, S.Y. (2022). 'Optimal Deformation of a Small Plastic Light-guide Using Machine Learning Algorithms', Materiale Plastice, 59(3), pp. 128-142. https://doi.org/10.37358/MP.22.3.5611

IEEE
M.J. Yoo, S.Y. Han, "Optimal Deformation of a Small Plastic Light-guide Using Machine Learning Algorithms". Materiale Plastice, vol. 59, no. 3, pp. 128-142, 2022. [online]. https://doi.org/10.37358/MP.22.3.5611

Text
Min Ji Yoo, Seong Yeol Han,
Optimal Deformation of a Small Plastic Light-guide Using Machine Learning Algorithms,
Materiale Plastice,
Volume 59, Issue 3,
2022,
Pages 128-142,
ISSN 2668-8220,
https://doi.org/10.37358/MP.22.3.5611.
(https://revmaterialeplastice.ro/Articles.asp?ID=5611)
Keywords: injection molding; CAE; Decision tree; process parameters; deformation; optimization

RIS
TY - JOUR
T1 - Optimal Deformation of a Small Plastic Light-guide Using Machine Learning Algorithms
A1 - Yoo, Min Ji
A2 - Han, Seong Yeol
JF - Materiale Plastice
JO - Mater. Plast.
PB - Materiale Plastice SRL
SN - 2668-8220
Y1 - 2022
VL - 59
IS - 3
SP - 128
EP - 142
UR - https://doi.org/10.37358/MP.22.3.5611
KW - injection molding
KW - CAE
KW - Decision tree
KW - process parameters
KW - deformation
KW - optimization
ER -

BibTex
@article{MatPlast2022P128,
author = {Yoo Min Ji and Han Seong Yeol},
title = {Optimal Deformation of a Small Plastic Light-guide Using Machine Learning Algorithms},
journal = {Materiale Plastice},
volume = {59},
number = {3},
pages = {128-142},
year = {2022},
issn = {2668-8220},
doi = {https://doi.org/10.37358/MP.22.3.5611},
url = {https://revmaterialeplastice.ro/Articles.asp?ID=5611}
}

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