지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.9
- 수록면
- 222 - 231 (10page)
- DOI
- 10.7315/CDE.2023.222
이용수
초록· 키워드
오류제보하기
Existing analysis methods have limitations in identifying the exact cause of defects because several variables cause defects in a complex manner in the high-pressure die-casting process. However, as data processing speeds increase and analysis technologies advance, research activities are progressing on techniques to analyze complex manufacturing processes. In this study, numerical and image data were collected for the main variables that cause porosity defects in the die-casting process. Based on this, we intend to design a failure prediction algorithm using the HP-GAN(Hypothesis Pruning Generative Adversarial Network) algorithm and verify the algorithm. The HP-GAN algorithm is a combination of CNN(Convolutional Neural Network) and GAN algorithms. The raw data used in HP-GAN are line data derived from the die-casting equipment PLC and thermal images taken before and after spraying on the mold work surface through a thermal imaging camera. data, porosity defect data. To strengthen the algorithm, we used the Mean Squared Error (MSE) formula and the Gradient Decent Algorithm (GDA) to modify the weights of the algorithm to increase the prediction accuracy.
목차
ABSTRACT
1. 서론
2. 다이캐스팅 데이터 수집
3. 기공 불량 예측 모델
4. 결론
References
참고문헌 (21)
참고문헌 신청
Yu, S.H., Lee, H.J., Park, T.J., Yoon, J.W. and Seo, K.W., 2021, A Comparative Study on the Performance of Deep Learning Model for Small Defects in Metal Precision Casting Products, Proceedings of HCI Society Conference, Korea, 1, pp.677-681, https://www.
Sowa, L., 2012, Numerical Modeling the Pressure Die Casting Process of an Angle Plate, Archives of Mechanical Technology and Automation, 32(1), pp.55-63, www.infona.pl/resource/bwmeta1. element.baztech-article-BPP3-0003-0068/tab/summary
Lee, J.H., Noh, S.D., Kim, H.J. and Kang, Y.S., 2018, Implementation of Cyber-physical Production Systems for Quality Prediction and Operation Control in Metal Casting, Sensors, 18(5), pp.1428, doi: 10.3390/s18051428
Yang, B.S., Oh, M.S. and Tan, A.C., 2008, Machine Condition Prognosis Based on Regression Trees and One-step-ahead Prediction, Mechanical Systems and Signal Processing, 22(5), pp.1179-1193, doi: 10.1016/j.ymssp.2007. 11.012
Ha, J.M., Kim, G.W., Lee, K.T., Oh, M.J. and Kwon, Y.K., 2022, CNN-based Ship Fluorescent Paint Thickness Measurement, Korean Journal of Computational Design and Engineering, 27(4), 471-480, doi: 10.7315/CDE.2022.471
Sowa, L., 2012, Numerical Modeling the Pressure Die Casting Process of an Angle Plate, Archives of Mechanical Technology and Automation, 32(1), pp.55-63, www.infona.pl/resource/bwmeta1. element.baztech-article-BPP3-0003-0068/tab/summary
Lee, J.H., Noh, S.D., Kim, H.J. and Kang, Y.S., 2018, Implementation of Cyber-physical Production Systems for Quality Prediction and Operation Control in Metal Casting, Sensors, 18(5), pp.1428, doi: 10.3390/s18051428
Yang, B.S., Oh, M.S. and Tan, A.C., 2008, Machine Condition Prognosis Based on Regression Trees and One-step-ahead Prediction, Mechanical Systems and Signal Processing, 22(5), pp.1179-1193, doi: 10.1016/j.ymssp.2007. 11.012
Ha, J.M., Kim, G.W., Lee, K.T., Oh, M.J. and Kwon, Y.K., 2022, CNN-based Ship Fluorescent Paint Thickness Measurement, Korean Journal of Computational Design and Engineering, 27(4), 471-480, doi: 10.7315/CDE.2022.471
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