Optimization of Plastic Speed Meter Housing for Automobiles: Injection Molding Simulation, Taguchi Method and Machine Learning

Han, Seongryeol

Seongryeol Han

Optimization of Plastic Speed Meter Housing for Automobiles: Injection Molding Simulation, Taguchi Method and Machine Learning

Abstract:
The plastic speed meter housing for automobiles requires accurate parts and assembly to inform the driver of their exact speed. For accurate assembly, the molded speed meter should have a minimize amount of deformation. In this study, to obtain injection molding conditions that minimize the deformation of the speed meter, the main molding conditions that cause the deformation of the speed meter were identified using the Taguchi method. By combining the confirmed molding conditions, 150 data sets were created, and machine learning was conducted using the data set. The model with the best accuracy learned through machine learning was the Linear Regression model. The results of this Linear Regression model were then validated with test data. The optimal injection molding conditions were derived by inputting 5000 molding conditions data into the learned Linear Regression model. Injection molding analysis was performed using the derived injection molding conditions, and the amount of deformation was reduced by about 6.4% compared to the case where current molding conditions were applied. The optimal molding conditions obtained by machine learning were applied to actcual molding. The amount of deformation of the mold amount of the molded speed meter housing was smaller than the amount of deformation predicted in the machine learning model.

Keywords:
injection molding simulation; Taguchi method; machine learning; Linear Regression; plastic speed meter housing

Issue: 2023 Volume 60, Issue 3

Pages: 58-72

Publication date: 2023/10/4

https://doi.org/10.37358/MP.23.3.5676

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Cite this article as:
HAN, S., , Optimization of Plastic Speed Meter Housing for Automobiles: Injection Molding Simulation, Taguchi Method and Machine Learning, Mater. Plast., 60(3), 2023, 58-72. https://doi.org/10.37358/MP.23.3.5676

Vancouver
Han S. Optimization of Plastic Speed Meter Housing for Automobiles: Injection Molding Simulation, Taguchi Method and Machine Learning. Mater. Plast.[internet]. 2023 Jul;60(3):58-72. Available from: https://doi.org/10.37358/MP.23.3.5676

APA 6th edition
Han, S., (2023). Optimization of Plastic Speed Meter Housing for Automobiles: Injection Molding Simulation, Taguchi Method and Machine Learning. Materiale Plastice, 60(3), 58-72. https://doi.org/10.37358/MP.23.3.5676

Harvard
Han, S., (2023). 'Optimization of Plastic Speed Meter Housing for Automobiles: Injection Molding Simulation, Taguchi Method and Machine Learning', Materiale Plastice, 60(3), pp. 58-72. https://doi.org/10.37358/MP.23.3.5676

IEEE
S. Han, "Optimization of Plastic Speed Meter Housing for Automobiles: Injection Molding Simulation, Taguchi Method and Machine Learning". Materiale Plastice, vol. 60, no. 3, pp. 58-72, 2023. [online]. https://doi.org/10.37358/MP.23.3.5676

Text
Seongryeol Han,
Optimization of Plastic Speed Meter Housing for Automobiles: Injection Molding Simulation, Taguchi Method and Machine Learning,
Materiale Plastice,
Volume 60, Issue 3,
2023,
Pages 58-72,
ISSN 2668-8220,
https://doi.org/10.37358/MP.23.3.5676.
(https://revmaterialeplastice.ro/Articles.asp?ID=5676)
Keywords: injection molding simulation; Taguchi method; machine learning; Linear Regression; plastic speed meter housing

RIS
TY - JOUR
T1 - Optimization of Plastic Speed Meter Housing for Automobiles: Injection Molding Simulation, Taguchi Method and Machine Learning
A1 - Han, Seongryeol
JF - Materiale Plastice
JO - Mater. Plast.
PB - Materiale Plastice SRL
SN - 2668-8220
Y1 - 2023
VL - 60
IS - 3
SP - 58
EP - 72
UR - https://doi.org/10.37358/MP.23.3.5676
KW - injection molding simulation
KW - Taguchi method
KW - machine learning
KW - Linear Regression
KW - plastic speed meter housing
ER -

BibTex
@article{MatPlast2023P58,
author = {Han Seongryeol},
title = {Optimization of Plastic Speed Meter Housing for Automobiles: Injection Molding Simulation, Taguchi Method and Machine Learning},
journal = {Materiale Plastice},
volume = {60},
number = {3},
pages = {58-72},
year = {2023},
issn = {2668-8220},
doi = {https://doi.org/10.37358/MP.23.3.5676},
url = {https://revmaterialeplastice.ro/Articles.asp?ID=5676}
}

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