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Drying lumber with little variation in its target moisture content leads to increased cost savings and reduced wet claims. The variability of moisture content is typically monitored using quality control charts, which graph process central tendency (average) and process dispersion (standard deviation) over time. The usual assumption in constructing and using these charts is that the moisture content follows a normal (Gaussian) statistical distribution. However, due to the nature of the kilndrying process, the resulting lumber moisture content distribution is often leftbounded and asymmetrical, which is characteristic of a lognormal distribution. In these cases, the normality assumption can lead to errors such as false outofcontrol signals. This book outlines graphical and analytical goodnessoffit methods for assessing the (non)normality of lumber moisture content data, discusses appropriate monitoring parameters for process central tendency and dispersion, and presents practical procedures for calculating the centerline and upper/lower control limits of lognormal control charts.
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