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http://dx.doi.org/10.5762/KAIS.2021.22.4.580

A Study on the Improvement of Injection Molding Process Using CAE and Decision-tree  

Hwang, Soonhwan (Department of Smart Convergence Consulting, Hansung University)
Han, Seong-Ryeol (Department of Metalmold Design Engineering, Kongju National University)
Lee, Hoojin (Department of Smart Convergence Consulting, Hansung University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.22, no.4, 2021 , pp. 580-586 More about this Journal
Abstract
The CAT methodology is a numerical analysis technique using CAE. Recently, a methodology of applying artificial intelligence techniques to a simulation has been studied. A previous study compared the deformation results according to the injection molding process using a machine learning technique. Although MLP has excellent prediction performance, it lacks an explanation of the decision process and is like a black box. In this study, data was generated using Autodesk Moldflow 2018, an injection molding analysis software. Several Machine Learning Algorithms models were developed using RapidMiner version 9.5, a machine learning platform software, and the root mean square error was compared. The decision-tree showed better prediction performance than other machine learning techniques with the RMSE values. The classification criterion can be increased according to the Maximal Depth that determines the size of the Decision-tree, but the complexity also increases. The simulation showed that by selecting an intermediate value that satisfies the constraint based on the changed position, there was 7.7% improvement compared to the previous simulation.
Keywords
Artificial Neural Networks; Computer Aided Engineering; Decision-tree; Injection Molding; Multi-Layer Perceptron; Warpage;
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