1998 data bank for kiln-dried red oak lumber

Gatchell, Charles J.; Thomas, R. Edward; Walker, Elizabeth

doi:10.2737/ne-gtr-245

Cited by 8 publications

(2 citation statements)

References 2 publications

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“…Lumber.-No. 1 Common kiln-dried red oak lumber contained in the digital red oak data bank created by Gatchell et al (1998) was used for the simulation tests. The board size and quality distribution published by Wiedenbeck et al (2003) was used to create the lumber data files.…”

Section: Lumber Cut-up Simulationmentioning

confidence: 99%

A Regression-Based Method for Estimating Rip-First Rough-Mill Lumber Yield

Buehlmann¹,

Kline²,

Wiedenbeck³

2009

Forest Products Journal

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Estimating yield from lumber cut-up in rip-first rough mills for material management and job costing purposes is uncertain unless simulation models are used. To augment the toolbox for industry practitioners, a novel yield estimation model was derived using linear least squares techniques and data derived from an orthogonal, 2 20-11 fractional factorial design of resolution V.The model estimated 450 of 512 cutting bills tested within 1 percent absolute yield. However, cutting bills that do not adhere to the model's framework suffer a larger estimation error. The least squares estimation model thus is a helpful tool in ranking cutting bills that adhere to the model's framework for their expected yield levels and facilitates the selection of part sizes to be included in cutting bills. Further research is needed to make the model useful for a wider range of cutting bills.

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Section: Lumber Cut-up Simulationmentioning

confidence: 99%

A Regression-Based Method for Estimating Rip-First Rough-Mill Lumber Yield

Buehlmann¹,

Kline²,

Wiedenbeck³

2009

Forest Products Journal

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“…The grades used in this study were FAS, SEL, 1 Common, 2A Common and 3A Common lumber [18]. For each grade combination of the experiment, three lumber samples were randomly collected from the 1998 kiln-dried red oak data bank [26] using the MAKEFILE tool in RR2 [23].…”

Section: Lumbermentioning

confidence: 99%

Linear programming and optimizing lumber quality composition in secondary hardwood dimension mills

Buehlmann

Zuo

Thomas

2004

Proceedings of the Institution of Mechanical Engineers, Part B:

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Linear programming has been widely applied in the secondary hardwood dimension manufacturing industries to solve the least-cost lumber grade mix problem, which refers to the search for minimal raw material costs. Most existing models are based on the assumption of a simple linear relationship between lumber grade mix and yield. However, this crucial assumption has never been veri®ed.In this study, the results from a ®ve-factor mixture design statistically proved that none of the cutting bills tested has a simple linear relationship between yield and di erent lumber grade mixes. It was observed that cutting bill characteristics and lumber quality a ect the relationship between yield and lumber grades. Cutting bills that require wider and/or longer parts tend to behave non-linearly. In addition, the more dissimilar lumber grade qualities that are processed, the more likely is the occurrence of a non-linear response.The inability to predict the relationship between yield and lumber grades, coupled with the high percentage of non-simple linear relationships observed in this study, brings into question the validity of the linearity assumption applied in previous linear programming models. Further e orts are needed to construct a new least-cost lumber grade mix model that does not rely on the assumption of a simple linear relationship between lumber grade mix and yield.

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