과제정보
본 연구는 중소기업벤처부의 스마트제조혁신기술개발사업(Project No. SE230069)의 지원으로 진행되었습니다.
참고문헌
- Mok, S. L., Kwong, C. K., Grasser, F., D'Arrigo, A., and Colombi, S., "Application of artificial neural network and fuzzy logic in a case-based system for initial process parameter setting of injection molding", J. Intell. Manuf., Vol. 13, No. 3, pp. 165-176, 2002. https://doi.org/10.1023/A:1015730705078
- Moon, Y. D., "Process optimization for the steam injection molding", Design & Manufacturing, Vol. 9, No. 2, pp. 10-15, 2015.
- Chen, J. Y., Yang, K. J., and Huang, S. M., "online quality monitoring of molten resin in injection molding", Int. J. Heat Mass Transfer, Vol. 122, pp. 681-693, 2018. https://doi.org/10.1016/j.ijheatmasstransfer.2018.02.019
- Lee, S. H., and Lee, H. S., "Rapid cooling of injection mold for high-curvature parts using CO2 cooling module", Design & Manufacturing, Vol. 16, No. 4, pp. 67-74, 2022.
- Lee, J. H., and Kim, J. S., "A study on monitoring for process time and process properties by measuring vibration signals transmitted to the mold during injection molding", Design & Manufacturing, Vol. 14, No. 3, pp. 8-16, 2020.
- Lee, J. H., and Kim, J. S., "Development and evaluation of edge devices for injection molding monitoring", Design & Manufacturing, Vol. 14, No. 4, pp. 35-39, 2020.
- Chris, Y., and Kim, J. S., "A study on the monitoring of cooling time using the change in the magnitude of mold vibration in injection molding", Design & Manufacturing, Vol. 15, No. 3, pp. 45-49, 2021.
- Lee, J. H., and Kim, J. S., "A Study on Process Characterization based on Vibration Signals Transmitted to the Mold in the Press Molding Process", Design & Manufacturing, Vol. 17, No. 1, pp. 56-63, 2023.
- Xia, P., and Zhang, L., "Learning similarity with cosine similarity ensemble", Inf. Sci., Vol. 307, No. 20, pp. 39-52, 2015. https://doi.org/10.1016/j.ins.2015.02.024
- Kalhori, H., Alamadari, M. M., and Ye, L., "Automated algorithm for impact force identification using cosine similarity searching", Meas., Vol. 122, pp. 648-657, 2018. https://doi.org/10.1016/j.measurement.2018.01.016