Establishing Multivariate Specification Regions for Incoming Materials

References

• Anderson, T. W. (1984). An Introduction to Multivariate Statistical Analysis 2nd ed. John Wiley and Sons, New York.
   Google Scholar
• Automotive Division of the ASQC (1991). “Fundamental Statistical Process Control Reference Manual”. Prepared by Quality and Supplier Assessment Staffs at Chrysler, Ford and General Motors under the auspices of the Automotive Division of the ASQC in collaboration with the Automotive Industry Action Group, Copyright A.I.A.G.
   Google Scholar
• Brinkley, S. (1991). “The Team Way to Quality Raw Materials”. ASQC Quality Congress Transactions, Milwaukee, WI, pp. 497–500.
   Google Scholar
• Burnham, A. J.; MacGregor, J. F.; and Viveros, R. (1999). “Latent Variable Multivariate Regression Modeling”. Chemometrics and Intelligent Laboratory Systems 48, pp. 167–180.
   Web of Science ®Google Scholar
• Burnham, A. J.; Viveros, R.; and MacGregor, J. F. (1996). “Frameworks for Latent Variable Multivariate Regression”. Journal of Chemometrics 10, pp. 31–45.
   Web of Science ®Google Scholar
• De Smet, J. A. (1993). “Development of Multivariate Specification Limits using Partial Least Squares Regression”. Masters Thesis, McMaster University, Hamilton, Ontario, Canada.
   Google Scholar
• Derringer, G. and Suich, R. (1980). “Simultaneous Optimization of Several Response Variables”. Journal of Quality Technology 12, pp. 214–219.
   Web of Science ®Google Scholar
• Duchesne, C. (2000). “Improvement of Processes and Product Quality through Multivariate Data Analysis”. Ph.D. Thesis, McMaster University, Hamilton, Ontario, Canada.
   Google Scholar
• Eriksson, L.; Johansson, E.; Kettaneh-Wold, N.; and Wold, S. (2001). Multi- and Megavariate Data Analysis: Principles and Applications. Umetrics Academy, Sweden.
   Google Scholar
• Fathi, Y. (1990). “Producer-Consumer Tolerances”. Journal of Quality Technology 22, pp. 138–145.
   Web of Science ®Google Scholar
• Geladi, P. and Kowalski, B. R. (1986). “Partial Least-Squares Regression: A Tutorial”. Analytica Chemica Acta 185, pp. 1–17.
   Web of Science ®Google Scholar
• Harrington, E. C. (1965). “The Desirability Function”. Industrial Quality Control April, pp. 494–498.
   Google Scholar
• Jackson, J. E. and Mudholkar, G. S. (1979). “Control Procedures for Residuals Associated with Principal Component Analysis”. Technometrics 21, pp. 341–349.
   Web of Science ®Google Scholar
• Jaeckle, C. M. and MacGregor, J. F. (1998). “Product Design through Multivariate Statistical Analysis of Process Data”. AIChE Journal 44, pp. 1105–1118.
   Web of Science ®Google Scholar
• Jaeckle, C. M. and MacGregor, J. F. (2000). “Industrial Applications of Product Design through the Inversion of Latent Variable Models”. Chemometrics and Intelligent Laboratory Systems 50, pp. 199–210.
   Web of Science ®Google Scholar
• Kourti, T. and MacGregor, J. F. (1996). “Multivariate SPC Methods for Process and Product Monitoring”. Journal of Quality Technology 28, pp. 409–428.
   Web of Science ®Google Scholar
• Kresta, J.; MacGregor, J. F.; and Marlin, T. (1991). “Multivariate Statistical Monitoring of Process Operating Performance”. Canadian Journal of Chemical Engineering 69, pp. 35–47.
   Web of Science ®Google Scholar
• MacGregor, J. F. (1997). “Using On-Line Process Data to Improve Quality: Challenges for Statisticians”. International Statistical Review 65, pp. 309–323. Also published in the Youden Address in the ASQC Statistical Division Newsletter, 16, No. 2 (1996).
   Web of Science ®Google Scholar
• MacGregor, J. F.; Jaeckle, C; Kiparissides, C; and Koutoudi, M. (1994). “Process Monitoring and Diagnosis by Multi-Block PLS Methods”. AIChE Journal 40, pp. 826–838.
   Web of Science ®Google Scholar
• Martens, H. and Martens, M. (2001). Multivariate Analysis of Quality: An Introduction. John Wiley and Sons, New York, NY.
   Google Scholar
• Martens, H. and Naes, T. (1989). Multivariate Calibration. John Wiley and Sons, Chichester, UK.
   Google Scholar
• Miller, P.; Swanson, R. E.; and Heckler, C. F. (1998). “Contribution Plots: A Missing Link in Multivariate Quality Control”. Applied Mathematics and Computer Science 8, pp. 775–782.
   Google Scholar
• Nelson, P. R. C; Taylor, P. A.; and MacGregor, J. F. (1996). “Missing Data Methods in PCA and PLS: Score Calculations with Incomplete Observations”. Chemometrics and Intelligent Laboratory Systems 35, pp. 45–65.
   Web of Science ®Google Scholar
• Nomikos, P. and MacGregor, J. F. (1995). “Multivariate SPC Charts for Monitoring Batch Processes”. Technometrics 37, pp. 41–59.
   Web of Science ®Google Scholar
• Polydynamics (1996). B-Filmcad 1.0, Polydynamics Inc., 1685 Main St. West, Suite 305, Hamilton, Ontario, Canada L8S 1G5.
   Google Scholar
• Redlich, H. A. (1996). “Hands-on Guide for Controlling Quality of Raw Materials”. The American Ceramic Society Bulletin 75, pp. 71–72.
   Web of Science ®Google Scholar
• Ryan, T. P. (2000). Statistical Methods for Quality Improvement 2nd ed. John Wiley and Sons, New York, NY.
   Google Scholar
• Sidiropoulos, V. (1995). “Comparison of Experiments with a Model of the Blown Film Process”. Masters thesis, University McMaster, Hamilton, Ontario, Canada.
   Google Scholar
• Stahle, L. and Wold, S. (1987). “Partial Least Squares Analysis with Cross-Validation for the Two-Class Problem: A Monte Carlo Study”. Journal of Chemometrics 1, pp. 185–196.
   Google Scholar
• Taguchi, G. (1986). Introduction to Quality Engineering. Quality Resources, White Plains, New York.
   Google Scholar
• Umetrics (1998). SIMCA-P 7.0, Umetrics Inc., 371 Highland Ave., Winchester, MA01890, U.S.A.
   Google Scholar
• Wade, M. R. and Woodall, W. H. (1993). “A Review and Analysis of Cause-Selecting Control Charts”. Journal of Quality Technology 25, pp. 161–169.
   Web of Science ®Google Scholar
• Wold, H. (1975). “Soft Modelling by Latent Variables: The Nonlinear Iterative Partial Least Squares Approach” in Perspectives in Probability and Statistics, Papers on Honour of M.S. Bartlett edited by J. Gani, Academic Press, London, UK.
   Google Scholar
• Wold, H. (1984). “PLS Regression” in Encyclopedia of Statistical Sciences, Vol. 6 edited by N.L. Johnson and S. Kotz, Wiley, New York, NY.
   Google Scholar
• Wold, S. (1978). “Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models”. Technometrics 20, pp. 397–405.
   Web of Science ®Google Scholar
• Wold, S.; Sjostrom, M.; Carlson, R.; Lundstedt, T.; Hellberg, S.; Skagerberg, B.; Wiksstrom, C; and Ohman, J. (1986). “Multivariate Design”. Analytica Chemica Acta 191, pp. 17–32.
   Web of Science ®Google Scholar
• Yacoub, F. and MacGregor, J. F. (2003). “Analysis and Optimization of a Polyurethane Injection Molding Process using Multivariate Projection Methods”. Chemometrics and Intelligent Laboratory Systems 65, pp. 17–33.
   Web of Science ®Google Scholar
• Yarborough, M. J. (1995a). “Raw Material Specifications”. The American Ceramic Society Bulletin 74, pp. 49–50.
   Web of Science ®Google Scholar
• Yarborough, M. J. (1995b). “Raw Material Specifications”. Ceramic Engineering and Science Proceedings 16, pp. 298–301.
   Google Scholar
• Zhang, G. X. (1984). “A New Type of Control Chart and a Theory of Diagnosis with Control Charts”. ASQC Annual Quality Congress Transactions, Milwaukee, WI, pp. 175–185.
   Google Scholar