MIXED-MODEL DEVELOPMENT FOR REAL-TIME STATISTICAL PROCESS CONTROL DATA IN WOOD PRODUCTS MANUFACTURING

Recent advances in laser technology have brought about great change in the area of statistical process control (SPC) for automated lumber manufacturing. SPC was traditionally based on a simple sampling scheme with measurements described by a one- way analysis of variance model. The introduction of multiple laser sensors capable of measuring multiple sides of each and every board processed has necessitated the derivation of a model to take into account multiple sources of variation with strongly autocorrelated errors. Autoregressive integrated moving average (ARIMA) models and seasonal fractional ARIMA (SARFIMA) models were useful in describing the short- and long-range dependence in the data. Although an autocorrelated errors model better describes the process, for use in many SPC applications, a simple uncorrelated errors model may suffice.

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