SPC methods for detecting simple sawing defects using real-time laser range sensor data

Wood and Fiber Science

Volumn 38, Issue 4, 2006, Pages 696-716

  Staudhammer, Christina ^a   Kozak, Robert A ^b   Maness, Thomas C ^b  

^a UNIVERSITY OF FLORIDA   (United States)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Control charts; Lumber manufacturing; Lumber size control; Real time data collection; Simulation; Statistical process control (SPC)

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; DEFECTS; LUMBER; REAL TIME SYSTEMS; SENSOR DATA FUSION; STATISTICAL PROCESS CONTROL;

CONTROL CHARTS; LUMBER MANUFACTURING; LUMBER SIZE CONTROL; REAL-TIME DATA COLLECTION;

SAWING;

ALGORITHMS; DEFECTS; LUMBER; PROCESS CONTROL; SENSORS; SIMULATION; STATISTICAL METHODS;

EID: 33751219238     PISSN: 07356161     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (30)

1. Burr, I. W. 1967. The effect of non-normality on constants for X-bar and R charts. Industrial Quality Control 24:563-569.
2. Cook, D. 1992. Statistical process control for continuous forest producers manufacturing operations. Forest Prod. J. 42(7/8):47-53.
3. Does, R. J. M. M., K. C. B. Roes, and A. Trip. 1999. Handling multivariate problems with univariate control charts. J. Chemometrics 13:353-369.
4. Gaylor, D. W., and F. N. Hopper. 1969. Estimating the degrees of freedom for linear combinations of mean squares by Satterthwaite's formula. Technometrics 11(4):691-706.
5. Gilbert, K. C,, K. Kirby, and C. R. Hild. 1997. Charting autocorrelated data: guidelines for practitioners. Quality Eng. 9(3):367-382.
6. Grimshaw, S. D., and F. B. Alt. 1997. Control charts for quantile function values. J. Quality Technol. 29(1):1-7.
7. Harter, H. L. 1960. Tables of range and studentized range. Ann. Math. Statist. 31(4):1122-1147.
8. Lu, C., and M. R. Reynolds. 1999. Control charts for monitoring the mean and variance of autocorrelated processes. J. Quality Technol. 31(3):259-274.
9. Maness, T. C. 1993. Real-time quality control system for automated lumbermills. Forest Prod. J. 43(7/8):17-22.
10. Maness, T. C., R. A. Kozak, and C. L. Staudhammer. 2003. Applying real-time statistical process control to manufacturing processes exhibiting between and within part size variability in the wood products industry. Quality Eng. 16(1):113-125.
11. Maness, T. C., R. A. Kozak, and C. L. Staudhammer. 2004. Reliability testing of statistical process control procedures for manufacturing with multiple sources of variation. Wood Fiber Sci. 36(3):443-458.
12. Mastrangelo, C. M., J. M. Porter, and R. V. Baxley. 2001. Multivariate process monitoring for nylon fiber production. Pages 223-246 in Frontiers in Statistical Quality Control. T. Wilrich, ed. Springer-Verlag, New York, NY.
13. Montgomery, D. C. 2001. Introduction to Statistical Quality Control. John Wiley & Sons, New York, NY.
14. Montgomery, D. C. and C. M. Mastrangelo. 1991. Some statistical process control methods for autocorrelated data. J. Quality Technol. 23:179-193.
15. Nelson, L. S. 1999. Notes on the Shewhart control chart. J. Quality Technol. 31(1):124-126.
16. Noffsinger, J. R., and R. B. Anderson. 2002. Effect of antocorrelation on composite panel production monitoring and control: a comparison of SPC techniques. Forest Prod. J. 52(3):60-67.
17. Padgett, W. J., and J. D. Spurrier. 1990. Shewhart-type charts for percentiles of strength distributions. J. Quality Technol. 22(4):283-290.
18. Pardo-Iguzquiza, E., and P. A. Dowd. 2004. Normality tests for spatially correlated data. Math. Geol. 36(6):659-681.
19. Rasmussen, H. K., R. A. Kozak, and T. C. Maness. 2004. An analysis of machine caused lumber shape defects in British Columbia sawmills. Forest Prod. J. 54(6):47-56.
20. Rigdon, S. E., E. N. Cruthis, and C. W. Champ. 1994. Design strategies for individuals and moving range control charts. J. Quality Technol. 26(4):274-287.
21. SAS Institute. 2002. SAS/STAT User's Guide, Version 8. 8.2, SAS Publishing, Cary, NC.
22. Shewhart, W. A. 1931. Economic control of quality of manufactured product. Van Nostrand, New York, NY.
23. Staudhammer, C. L. 2004. Statistical procedures for development of real-time statistical process control (SPC) systems in lumber manufacturing. Ph.D. Thesis, Department of Wood Science, University of British Columbia, Vancouver, BC, Canada.
24. Staudhammer, C. L., V. LeMay, T. C. Maness, and R. A. Kozak. 2005. Mixed-model development of real-time statistical process control data in wood products Manufacturing. Forest Biometry, Modelling, and Information Sciences 1:19-35.
25. Sillivan, J. H., and W. H. Woodall. 1996. A control chart for the preliminary analysis of individual observations. J. Quality Technol. 28(3):265-278.
26. Wheeler, D. J. 1995. Advanced topics in statistical process control. SPC Press, Knoxville, TN.
27. Woodall, W. H., R. W. Hoerl, A. C. Palm, and D. J. Wheeler. 2000. Controversies and contradictions in statistical process control. J. Quality Technol. 32(4):341-350.
28. Young, T. M., and P. M. Winistorfer. 1999. SPC. Forest Prod. J. 49(3):10-17.
29. Young, T. M., and P. M. Winistorfer. 2001. The effects of autocorrelation on real-time statistical process control with solutions for forest products manufacturers. Forest Prod. J. 51(11/12): 70-77.
30. Young, T. M., and P. M. Winistorfer, and S. Wang. 1999. Multivariate control charts of MDF and OSB vertical density profile attributes. Forest Prod. J. 49(5):79-86.