Simultaneous optimization of quality and reliability characteristics through designed experiment

References

• Antony, J. 2000. Multi-response optimization in industrial experiments using Taguchi's quality loss function and principal component analysis. Quality and reliability engineering international 16 (1):3–8.
   Web of Science ®Google Scholar
• Antony, J. 2001. Simultaneous optimisation of multiple quality characteristics in manufacturing processes using Taguchi's quality loss function. The International Journal of Advanced Manufacturing Technology 17 (2):134–138.
   Web of Science ®Google Scholar
• Biles, W. E. 1975. A response surface method for experimental optimization of multi-response processes. Industrial & Engineering Chemistry Process Design and Development 14 (2):152–158.
   Web of Science ®Google Scholar
• Box, G. E., J. S. Hunter, and W. G. Hunter. 2005. Statistics for experimenters: design, innovation, and discovery, New Jersey: John Wiley & Sons.
   Google Scholar
• Box, G. E., and R. D. Meyer. 1986. An analysis for unreplicated fractional factorials. Technometrics 28 (1):11–18.
   Web of Science ®Google Scholar
• Box, G. E., and K. B. Wilson. 1951. On the Experimental Attainment of Optimum Conditions. Journal of the Royal Statistical Society 13 (1):1–45.
   Google Scholar
• Broyden, C. G. 1970. The convergence of a class of double-rank minimization algorithms 2. The new algorithm. IMA Journal of Applied Mathematics 6 (3):222–231.
   Google Scholar
• Candioti, L. V., M. M. De Zan, M. S. Camara, and H. C. Goicoechea. 2014. Experimental design and multiple response optimization. Using the desirability function in analytical methods development. Talanta 124:123– 138.
   PubMed Web of Science ®Google Scholar
• Chiao, C. H., and M. Hamada. 2001. Analyzing experiments with correlated multiple responses. Journal of Quality Technology 33 (4):451–465.
   Web of Science ®Google Scholar
• Derringer, G., and R. Suich. 1980. Simultaneous optimization of several response variables. Journal of Quality Technology 12 (4):214–219.
   Web of Science ®Google Scholar
• Fletcher, R. 1970. A new approach to variable metric algorithms. The computer journal 13 (3):317–322.
   Web of Science ®Google Scholar
• Freeman, L. J. 2011. A cautionary tale: small sample size concerns for grouped lifetime data. Quality Engineering 23 (2):134–141.
   Web of Science ®Google Scholar
• Freeman, L. J., and G. G. Vining. 2010. Reliability data analysis for life test experiments with subsampling. Journal of Quality Technology 42 (3):233–241.
   Web of Science ®Google Scholar
• Goldfarb, D. 1970. A family of variable-metric methods derived by variational means. Mathematics of computation 24 (109):23–26.
   Web of Science ®Google Scholar
• Harrington, E. C. 1965. The desirability function. Industrial quality control 21 (10):494–498.
   Google Scholar
• He, Z., J. Wang, J. Oh, and S. H. Park. 2010. Robust optimization for multiple responses using response surface methodology. Applied Stochastic Models in Business and Industry 26 (2):157–171.
   Web of Science ®Google Scholar
• He, Z., P. F. Zhu, and S. H. Park. 2012. A robust desirability function method for multi-response surface optimization considering model uncertainty. European Journal of Operational Research 221 (1):241–247.
   Web of Science ®Google Scholar
• Jiang, P., B. Guo, J. H. Lim, and M. J. Zuo. 2010. Group judgment of relationship between product reliability and quality characteristics based on Bayesian theory and expert's experience. Expert Systems with Applications 37 (10):6844–6849.
   Web of Science ®Google Scholar
• Jiang, R., and D. N. P. Murthy. 2009. Impact of quality variations on product reliability. Reliability Engineering & System Safety 94 (2):490–496.
   Web of Science ®Google Scholar
• Joseph, V. R., and I. T. Yu. 2006. Reliability improvement experiments with degradation data. IEEE Transactions on Reliability 55 (1):149–157.
   Web of Science ®Google Scholar
• Kensler, J. L., L. J. Freeman, and G. G. Vining. 2014. A practitioner's guide to analyzing reliability experiments with random blocks and subsampling. Quality Engineering 26 (3):359–369.
   Web of Science ®Google Scholar
• Korra, N. N., M. Vasudevan, K. R. Balasubramanian. 2015. Multi-objective optimization of activated tungsten inert gas welding of duplex stainless steel using response surface methodology. The International Journal of Advanced Manufacturing Technology 77 (1):67–81.
   Web of Science ®Google Scholar
• Lee, H., and D. H. Lee. 2016. A solution selection approach to multiresponse surface optimization based on a clustering method. Quality Engineering 28 (4):1–14.
   Web of Science ®Google Scholar
• Lio, Y. L., J. C. Lu, and L. Ruan. 2015. Robust parameter design for quality and reliability issues based on accelerated degradation measurements. IEEE Transactions on Reliability 64 (3):949–959.
   Web of Science ®Google Scholar
• Montgomery, D. C. 2009. Design and analysis of experiments, Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Myers, R. H., and W. H. Carter. 1973. Response surface techniques for dual response systems. Technometrics 15 (2):301–317.
   Web of Science ®Google Scholar
• Myers, R. H., D. C. Montgomery, and C. M. Anderson-Cook. 2016. Response surface methodology: process and product optimization using designed experiments, Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Myers, R. H., D. C. Montgomery, G. G. Vining, C. M. Borror, and S. M. Kowalski. 2004. Response surface methodology: a retrospective and literature survey. Journal of Quality Technology 36 (1):53–77.
   Web of Science ®Google Scholar
• Nocedal, J., and S. Wright. 2006. Numerical optimization, New York, NY: Springer Science & Business Media.
   Google Scholar
• Ortiz, Jr, F., J. R. Simpson, J. J. Pignatiello Jr, and A. Heredia-Langner. 2004. A genetic algorithm approach to multiple-response optimization. Journal of Quality Technology 36 (4):432–450.
   Web of Science ®Google Scholar
• Robinson, T. J., R. H. Myers, and D. C. Montgomery. 2004. Analysis considerations in industrial split-plot experiments with non-normal responses. Journal of Quality Technology 36 (2):180–192.
   Web of Science ®Google Scholar
• Shanno, D. F. 1970. Conditioning of quasi-Newton methods for function minimization. Mathematics of Computation 24 (111):647–656.
   Web of Science ®Google Scholar
• Taguchi, G. 1986. Introduction to quality engineering: designing quality into products and processes, Tokyo: Asian Productivity Organization.
   Google Scholar
• Tong, L. I., C. T. Su, and C. H. Wang. 1997. The optimization of multi-response problems in the Taguchi method. International Journal of Quality & Reliability Management 14 (4):367–380.
   Google Scholar
• Vining, G. G. 1998. A compromise approach to multiresponse optimization. Journal of Quality Technology 30 (4):309–313.
   Web of Science ®Google Scholar
• Vining, G. G. 2013. Technical advice: experimental protocol and the basic principles of experimental design. Quality Engineering 25 (3):307–311.
   Web of Science ®Google Scholar
• Vining, G. G., L. J. Freeman, and J. L. Kensler. 2015. An overview of designing experiments for reliability data. In Frontiers in Statistical Quality Control 11:321–336.
   Google Scholar
• Vining, G. G., S. M. Kowalski, and D. C. Montgomery. 2005. Response surface designs within a split-plot structure. Journal of Quality Technology 37 (2):115–129.
   Web of Science ®Google Scholar
• Wang, C. H., and Y. Hsu. 2009. Enhancing rubber component reliability by response model. Computers & Industrial Engineering 57 (3):806–812.
   Web of Science ®Google Scholar
• Wang, G., Z. Niu, and Z. He. 2015. Bias reduction of MLEs for Weibull distributions under grouped lifetime data. Quality Engineering 27 (3):341–352.
   Web of Science ®Google Scholar
• Wang, P., G. F. Tsai, and A. Qu. 2012. Conditional inference functions for mixed-effects models with unspecified random-effects distribution. Journal of the American Statistical Association 107 (498):725–736.
   Web of Science ®Google Scholar
• Wu, C. J., and M. S. Hamada. 2009. Experiments: planning, analysis, and optimization, New Jersey: John Wiley & Sons.
   Google Scholar
• Yadav, O. P., S. S. Bhamare, and A. Rathore. 2010. Reliability‐based robust design optimization: A multi‐objective framework using hybrid quality loss function. Quality and Reliability Engineering International 26 (1):27–41.
   Web of Science ®Google Scholar