Design & Manufacturing

Korean Society of Die & Mold Engineering (한국금형공학회)
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A study on the comparison of the predicting performance of quality of injection molded product according to the structure of artificial neural network

인공신경망 구조에 따른 사출 성형폼 품질의 예측성능 차이에 대한 비교 연구

  • Yang, Dong-Cheol (Shape Manufacturing R D Department, Korea Institute of Industrial Technology) ;
  • Lee, Jun-Han (Shape Manufacturing R D Department, Korea Institute of Industrial Technology) ;
  • Kim, Jong-Sun (Shape Manufacturing R D Department, Korea Institute of Industrial Technology)
  • 양동철 (한국생산기술연구원 형상제조연구부문) ;
  • 이준한 (한국생산기술연구원 형상제조연구부문) ;
  • 김종선 (한국생산기술연구원 형상제조연구부문)
  • Received : 2021.03.19
  • Accepted : 2021.03.31
  • Published : 2021.03.31
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Abstract

The quality of products produced by injection molding process is greatly influenced by the process variables set on the injection molding machine during manufacturing. It is very difficult to predict the quality of injection molded product considering the stochastic nature of manufacturing process, because the process variables complexly affect the quality of the injection molded product. In the present study we predicted the quality of injection molded product using Artificial Neural Network (ANN) method specifically from Multiple Input Single Output (MISO) and Multiple Input Multiple Output (MIMO) perspectives. In order to train the ANN model a systematic plan was prepared based on a combination of orthogonal sampling and random sampling methods to represent various and robust patterns with small number of experiments. According to the plan the injection molding experiments were conducted to generate data that was separated into training, validation and test data groups to optimize the parameters of the ANN model and evaluate predicting performance of 4 structures (MISO1-2, MIMO1-2). Based on the predicting performance test, it was confirmed that as the number of output variables were decreased, the predicting performance was improved. The results indicated that it is effective to use single output model when we need to predict the quality of injection molded product with high accuracy.

Keywords

References

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