Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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A Study on Securing Multiple Quality Requirements of New Product Using Screening Design with a Case Study

선별실험계획을 활용한 신제품의 다수품질특성 확보 방안 : 사례 연구를 중심으로

  • Byun, Jai-Hyun (Department of Industrial and Systems Engineering and Engineering Research Institute Gyeongsang National University) ;
  • Lee, Ki-Chang (Department of Materials Science and Engineering and Engineering Research Institute Gyeongsang National University) ;
  • Suh, Pan Seok (Research and Business Development Team, Dong Sung Chemical) ;
  • Kwak, Kyung-Hwan (PPG Research Team, Kumho Petrochemical R&BD Center) ;
  • Jang, Sung Il (Research and Business Development Team, Dong Sung Chemical)
  • 변재현 (경상대학교 산업시스템공학부, 공학연구원) ;
  • 이기창 (경상대학교 나노신소재공학부, 공학연구원) ;
  • 서판석 (동성화학(주), R&BD 팀) ;
  • 곽경환 (금호화학, PPG 연구팀) ;
  • 장성일 (동성화학(주), R&BD 팀)
  • Received : 2017.01.10
  • Accepted : 2017.02.15
  • Published : 2017.04.15
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Abstract

For product or process design and development, it is common to optimize multiple responses (characteristics) based on experimental data. To determine optimal conditions, we need to design the experiment, estimate a proper model for each response, and optimize the multiple responses simultaneously. There are several techniques and many research results on optimizing multiple responses simultaneously, when the experimental data are available. However, the experimental design issue for optimizing multiple responses has not been discussed yet. This paper proposes some idea on how to plan screening design when requirements for multiple performance characteristics are to be met in developing new products. A screening design procedure is developed for securing the requirements of multiple responses. Initial design factors are classified into three categories; specific, non-conflicting common, and conflicting common. After screening experiments, follow-up design region search method is suggested with respect to the most unsatisfied or important response, or overall desirability. A case study on a synthesis of melamine formaldehyde resin is presented to illustrate the procedure and to show the validity of the approach.

Keywords

References

  1. Biles, W. E. (1975), A Response Surface Method for Experimental Optimization of Multiresponse Processes, Industrial and Engineering Chemistry, 14, 151-158. https://doi.org/10.1021/i360055a004
  2. Box, G. E. P., Hunter, S. J., and Hunter, W. G. (2005), Statistics for Experimenters : Design, Innovation, and Discovery, 2nd Edition, Wiley, New York.
  3. Brydson, J. A. (1982), Plastics Materials, 4th Edition, Butterworth Scientific, London.
  4. Byun, J.-H. and Kim, K.-J. (1998), A Desirability Function Approach to the Robust Design for Multiple Quality Characteristics, Journal of Korean Institute of Industrial Engineers, 24(2), 287-296.
  5. Del Castillo, E. (1996), A Nonlinear Programming Solution to the Dual Response Problem, Journal of Quality Technology, 25, 199-204.
  6. Derringer, G. and Suich, R. (1980), Simultaneous Optimization of Several Response Variables, Journal of Quality Technology, 12, 214-219. https://doi.org/10.1080/00224065.1980.11980968
  7. FDA (2004), Guideline for Industry : PAT - A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance, Food and Drug Authority.
  8. FDA (2006), Guideline for Industry : Investigating Out-of-Specification (OOS) Test Results for Pharmaceutical Production, Food and Drug Authority.
  9. Khuri, A. (1996), Multi-response Surface Methodology, in Handbook of Statistics : Design and Analysis of Experiments (Vol.13), edited by A. Ghosh and C. R. Rao, 377-406.
  10. Ko, Y. H., Kim, K. J., and Jun, C. H. (2005), A New Loss Function-Based Method for Multiresponse Optimization, Journal of Quality Technology, 37(1), 50-59. https://doi.org/10.1080/00224065.2005.11980300
  11. Montgomery, D. C. (2013), Design and Analysis of Experiments, 8th Edition, Wiley, New York.
  12. Myers, R. H. (1999), Response Surface Methodology-Current Status and Future Directions, Journal of Quality Technology, 31(1), 16-29. https://doi.org/10.1080/00224065.1999.11979890
  13. Myers, R. H. and Carter, W. H., Jr. (1973), Response Surface Techniques for Dual Response Systems, Technometrics, 15, 301-317. https://doi.org/10.1080/00401706.1973.10489044
  14. Myers, R. H., Montgomery, D. C., Vining, G. G., Borror, C. M., and Kowalski, S. M. (2004), Response Surface Methodology : a Retrospective and Literature Survey, Journal of Quality Technology, 36(1), 53-77. https://doi.org/10.1080/00224065.2004.11980252
  15. Otto, K. and Wood, K. (2001), Product Design : Techniques in Reverse Engineering and New Product Development, Prentice-Hall, New Jersey.
  16. Peterson, J. J., Snee, R. D., McAllister, P. R., Schofield, T. L., and Carella, A. J. (2009), Statistics in Pharmaceutical Development and Manufacturing (with Discussions), Journal of Quality Technology 41(2), 111-147. https://doi.org/10.1080/00224065.2009.11917764
  17. Phadke, M. S. (1989), Quality Engineering Using Robust Design, Prentice-Hall, New Jersey.
  18. Vining, G. G. (1998), A Compromise Approach to Multiresponse Optimization, Journal of Quality Technology, 30, 309-313. https://doi.org/10.1080/00224065.1998.11979867