Journal of the Korean Society for Precision Engineering (한국정밀공학회지)

Korean Society for Precision Engineering (한국정밀공학회)
권호 표지
DOI QR코드

DOI QR Code

Development of Real-Time Condition Diagnosis System Using LabVIEW for Lens Injection Molding Process

LabVIEW 를 활용한 실시간 렌즈 사출성형 공정상태 진단 시스템 개발

  • Na, Cho Rok (Department of Mechanical Engineering, Graduate School, Sungkyunkwan University) ;
  • Nam, Jung Soo (Department of Mechanical Engineering, Graduate School, Sungkyunkwan University) ;
  • Song, Jun Yeob (Department of Ultra Precision Machines and Systems, Korea Institute of Machinery and Materials) ;
  • Ha, Tae Ho (Department of Ultra Precision Machines and Systems, Korea Institute of Machinery and Materials) ;
  • Kim, Hong Seok (Department of Mechanical and Automotive Engineering, Seoul National University of Science and Technology) ;
  • Lee, Sang Won (School of Mechanical Engineering, Sungkyunkwan University)
  • 나초록 (성균관대학교 대학원 기계공학과) ;
  • 남정수 (성균관대학교 대학원 기계공학과) ;
  • 송준엽 (한국기계연구원 초정밀기계시스템연구실) ;
  • 하태호 (한국기계연구원 초정밀기계시스템연구실) ;
  • 김홍석 (서울과학기술대학교 기계.자동차공학과) ;
  • 이상원 (성균관대학교 기계공학부)
  • Received : 2015.02.06
  • Accepted : 2015.08.17
  • Published : 2016.01.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, a real-time condition diagnosis system for the lens injection molding process is developed through the use of LabVIEW. The built-in-sensor (BIS) mold, which has pressure and temperature sensors in their cavities, is used to capture real-time signals. The measured pressure and temperature signals are processed to obtain features such as maximum cavity pressure, holding pressure and maximum temperature by the feature extraction algorithm. Using those features, an injection molding condition diagnosis model is established based on a response surface methodology (RSM). In the real-time system using LabVIEW, the front panels of the data loading and setting, feature extraction and condition diagnosis are realized. The developed system is applied in a real industrial site, and a series of injection molding experiments are conducted. Experimental results show that the average real-time condition diagnosis rate is 96%, and applicability and validity of the developed real-time system are verified.

Keywords

References

  1. Chen, S. C., Chien, R. D., Lin, S. H., Li, M. C., and Chang, J. A., "Feasibility Evaluation of Gas-Assisted Heating for Mold Surface Temperature Control," International Communications in Heat and Mass Transfer, Vol. 36, No. 8, pp. 805-812, 2009.
  2. Chen, C. W., Tai, P. H., Wang, M. W., Deng, W. J., and Chen, C. T., "A Neural Network-Based Approach for Dynamic Quality Prediction in a Plastic Injection Molding Process," Expert Systems with Applications, Vol. 35, No. 3, pp. 843-849, 2008. https://doi.org/10.1016/j.eswa.2007.07.037
  3. Zhang, S., Dubay, R., and Charest, M., "A Principal Component Analysis Model-Based Predictive Controller for Controlling Part Warpage in Plastic Injection Molding," Expert Systems with Applications, Vol. 42, No. 6, pp. 2919-2927, 2015. https://doi.org/10.1016/j.eswa.2014.11.030
  4. Tsai, K. M., Hsieh, C. Y., and Lo, Y. C., "A Study of the Effects of Process Parameters for Injection Molding on Surface Quality of Optical Lenses," Journal of Materials Processing Technology, Vol. 209, No. 7, pp. 3469-3477, 2009. https://doi.org/10.1016/j.jmatprotec.2008.08.006
  5. Kuo, C. F. J., Huang, C. C., and Kuo, Y. L., "Analysis of Processing Parameters in Fabrication of Fresnel Lens Solar Collector," Energy Conversion and Management, Vol. 57, pp. 33-41, 2012. https://doi.org/10.1016/j.enconman.2011.12.003
  6. Beak, D. S., Nam, J. S., and Lee, S. W., "A Development of Feature Extraction and Condition Diagnosis Algorithm for Lens Injection Molding Process," J. Korean Soc. Precis. Eng., Vol. 31, No. 11, pp. 1031-1040, 2014. https://doi.org/10.7736/KSPE.2014.31.11.1031
  7. Bas, D. and Boyaci, I. H., "Modeling and Optimization I: Usability of Response Surface Methodology," Journal of Food Engineering, Vol. 78, No. 3, pp. 836-846, 2007. https://doi.org/10.1016/j.jfoodeng.2005.11.024
  8. Moore, L. J. and Sa, P., "Comparisons with the Best in Response Surface Methodology," Statistics and Probability Letters, Vol. 44, No. 2, pp. 189-194, 1999. https://doi.org/10.1016/S0167-7152(99)00008-5