Design & Manufacturing

Korean Society of Die & Mold Engineering (한국금형공학회)
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A study on the accuracy of multi-task learning structure artificial neural network applicable to multi-quality prediction in injection molding process

사출성형공정에서 다수 품질 예측에 적용가능한 다중 작업 학습 구조 인공신경망의 정확성에 대한 연구

  • Lee, Jun-Han (Molding and Metal Forming R&D Department, Korea Institute of Industrial Technology) ;
  • Kim, Jong-Sun (Molding and Metal Forming R&D Department, Korea Institute of Industrial Technology)
  • 이준한 (한국생산기술연구원 금형성형연구부문) ;
  • 김종선 (한국생산기술연구원 금형성형연구부문)
  • Published : 2022.09.30
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Abstract

In this study, an artificial neural network(ANN) was constructed to establish the relationship between process condition prameters and the qualities of the injection-molded product in the injection molding process. Six process parmeters were set as input parameter for ANN: melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time. As output parameters, the mass, nominal diameter, and height of the injection-molded product were set. Two learning structures were applied to the ANN. The single-task learning, in which all output parameters are learned in correlation with each other, and the multi-task learning structure in which each output parameters is individually learned according to the characteristics, were constructed. As a result of constructing an artificial neural network with two learning structures and evaluating the prediction performance, it was confirmed that the predicted value of the ANN to which the multi-task learning structure was applied had a low RMSE compared with the single-task learning structure. In addition, when comparing the quality specifications of injection molded products with the prediction values of the ANN, it was confirmed that the ANN of the multi-task learning structure satisfies the quality specifications for all of the mass, diameter, and height.

Keywords

Acknowledgement

본 연구는 산업통상자원부의 소재부품기술개발사업(Project. No. KM220119, 20013311)의 지원으로 진행되었습니다.

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