SPC Methods for Detecting Simple Sawing Defects Using Real-Time Laser Range Sensor Data
Keywords:
Lumber size control, statistical process control (SPC), control charts, real-time data collection, lumber manufacturing, simulationAbstract
Effective statistical process control (SPC) procedures can greatly enhance product value and yield in the lumber industry, ensuring accuracy and minimum waste. To this end, many mills are implementing automated real-time SPC with non-contact laser range sensors (LRS). These systems have, thus far, had only limited success because of frequent false alarms and have led to tolerances being set excessively wide and real problems being missed. Current SPC algorithms are based on manual sampling methods and, consequently, are not appropriate for the volume of data generated by real-time systems. The objective of this research was to establish a system for real-time LRS size control data for automated lumber manufacturing. An SPC system was developed that incorporated multi-sensor data, and new SPC charts were developed that went beyond traditional size control methods, simultaneously monitoring multiple surfaces and specifically targeting common sawing defects. In this paper, eleven candidate control charts were evaluated. Traditional X-bar and range charts are suggested, which were explicitly developed to take into account the components of variance in the model. Applying these methods will lead to process improvements for sawmills using automated quality control systems, so that machines producing defective material can be identified and prompt repairs made.References
Burr, I. W. 1967. The effect of non-normality on constants for X-bar and R charts. Industrial Quality Control24:563-569.nCook, D. 1992. Statistical process control for continuous forest producers manufacturing operations. Forest Prod. J.42(7/8):47-53.nDoes, R. J. M. M., K. C. B. Roes, and A. Trip. 1999. Handling multivariate problems with univariate control charts. J. Chemometrics13:353-369.nGaylor, D. W., and F. N. Hopper. 1969. Estimating the degrees of freedom for linear combinations of mean squares by Satterthwaite's formula. Technometrics11(4):691-706.nGilbert, K. C., K. Kirby, and C. R. Hild. 1997. Charting autocorrelated data: guidelines for practitioners. Quality Eng.9(3):367-382.nGrimshaw, S. D., and F. B. Alt. 1997. Control charts for quantile function values. J. Quality Technol.29(1):1-7.nHarter, H. L. 1960. Tables of range and studentized range. Ann. Math. Statist.31(4):1122-1147.nLu, C., and M. R. Reynolds. 1999. Control charts for monitoring the mean and variance of autocorrelated processes. J. Quality Technol.31(3):259-274.nManess, T. C. 1993. Real-time quality control system for automated lumbermills. Forest Prod. J.43(7/8):17-22.nManess, 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.nManess, 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.nMastrangelo, 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.nMontgomery, D. C. 2001. Introduction to Statistical Quality Control. John Wiley & Sons, New York, NY.nMontgomery, D. C., and C. M. Mastrangelo. 1991. Some statistical process control methods for autocorrelated data. J. Quality Technol.23:179-193.nNelson, L. S. 1999. Notes on the Shewhart control chart. J. Quality Technol.31(1):124-126.nNoffsinger, J. R., and R. B. Anderson. 2002. Effect of autocorrelation on composite panel production monitoring and control: a comparison of SPC techniques. Forest Prod. J.52(3):60-67.nPadgett, W. J., and J. D. Spurrier. 1990. Shewhart-type charts for percentiles of strength distributions. J. Quality Technol.22(4):283-290.nPardo-Iguzquiza, E., and P. A. Dowd. 2004. Normality tests for spatially correlated data. Math. Geol.36(6):659-681.nRasmussen, 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.nRigdon, 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.nSAS Institute. 2002. SAS/STAT User's Guide, Version 8. 8.2, SAS Publishing, Cary, NC.nShewhart, W. A. 1931. Economic control of quality of manufactured product. Van Nostrand, New York, NY.nStaudhammer, 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.nStaudhammer, 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 Sciences1:19-35.nSullivan, J. H., and W. H. Woodall. 1996. A control chart for the preliminary analysis of individual observations. J. Quality Technol.28(3):265-278.nWheeler, D. J. 1995. Advanced topics in statistical process control. SPC Press, Knoxville, TN.nWoodall, 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.nYoung, T. M., and P. M. Winistorfer. 1999. SPC. Forest Prod. J.49(3):10-17.nYoung, 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.nYoung, T. M., 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.n
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