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Taschenbuch. Condizione: Neu. Quality control of non-normal moisture content in kiln-dry lumber | Practical procedures for determining the distribution of moisture content and constructing specialized control charts for log-normal data | Catalin Ristea | Taschenbuch | 104 S. | Englisch | 2009 | LAP LAMBERT Academic Publishing | EAN 9783838321011 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 kiln-drying process, the resulting lumber moisture content distribution is often left-bounded and asymmetrical, which is characteristic of a log-normal distribution. In these cases, the normality assumption can lead to errors such as false out-of-control signals. This book outlines graphical and analytical goodness-of-fit 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 log-normal control charts. 104 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 kiln-drying process, the resulting lumber moisture content distribution is often left-bounded and asymmetrical, which is characteristic of a log-normal distribution. In these cases, the normality assumption can lead to errors such as false out-of-control signals. This book outlines graphical and analytical goodness-of-fit 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 log-normal control charts.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 kiln-drying process, the resulting lumber moisture content distribution is often left-bounded and asymmetrical, which is characteristic of a log-normal distribution. In these cases, the normality assumption can lead to errors such as false out-of-control signals. This book outlines graphical and analytical goodness-of-fit 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 log-normal control charts.