Design & Manufacturing

Korean Society of Die & Mold Engineering (한국금형공학회)
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Prediction of Weight of Spiral Molding Using Injection Molding Analysis and Machine Learning

사출성형 CAE와 머신러닝을 이용한 스파이럴 성형품의 중량 예측

  • Bum-Soo Kim (Department of Optical Engineering and Metal Mold, Kongju National University) ;
  • Seong-Yeol Han (Department of Digital Convergence Metalmold Engineering, Kongju National University)
  • 김범수 (공주대학교 광공학.금형공학과) ;
  • 한성열 (공주대학교 디지털융합금형공학과)
  • Received : 2023.02.15
  • Accepted : 2023.03.31
  • Published : 2023.03.31
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Abstract

In this paper, we intend to predict the mass of the spiral using CAE and machine learning. First, We generated 125 data for the experiment through a complete factor design of 3 factors and 5 levels. Next, the data were derived by performing a molding analysis through CAE, and the machine learning process was performed using a machine learning tool. To select the optimal model among the models learned using the learning data, accuracy was evaluated using RMSE. The evaluation results confirmed that the Support Vector Machine had a good predictive performance. To evaluate the predictive performance of the predictive model, We randomly generated 10 non-overlapping data within the existing injection molding condition level. We compared the CAE and support vector machine results by applying random data. As a result, good performance was confirmed with a MAPE value of 0.48%.

Keywords

References

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