Design & Manufacturing

  • 제14권3호
  • /
  • Pages.1-7
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  • 2020
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  • 2800-0323(pISSN)
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  • 2733-452X(eISSN)
한국금형공학회 (Korean Society of Die & Mold Engineering)
권호 표지

인공신경망을 활용한 사출성형품의 질량과 치수 예측에 관한 연구

A Study on the Prediction of Mass and Length of Injection-molded Product Using Artificial Neural Network

  • 양동철 (한국생산기술연구원 형상제조연구부문) ;
  • 이준한 (한국생산기술연구원 형상제조연구부문) ;
  • 김종선 (한국생산기술연구원 형상제조연구부문)
  • 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)
  • 투고 : 2020.07.28
  • 심사 : 2020.09.30
  • 발행 : 2020.09.30
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초록

This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network (ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The input parameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANN model. The generated training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter of the ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of the injection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length, the average deviation of ANN was 0.043 mm.

키워드

과제정보

본 연구는 산업통상자원부의 기계산업핵심기술개발사업(Project No. KM200224, 10067766)의 지원으로 진행되었습니다.

참고문헌

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