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    Author(s): R. Edward Thomas
    Date: 2011
    Source: In: Fei, Songlin; Lhotka, John M.; Stringer, Jeffrey W.; Gottschalk, Kurt W.; Miller, Gary W., eds. Proceedings, 17th central hardwood forest conference; 2010 April 5-7; Lexington, KY; Gen. Tech. Rep. NRS-P-78. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station: 77-82.
    Publication Series: General Technical Report - Proceedings
    Station: Northern Research Station
    PDF: View PDF  (147.62 KB)

    Description

    The type, size, and location of internal defects dictate the grade and value of lumber sawn from hardwood logs. However, acquiring internal defect knowledge with x-ray/computed-tomography or magnetic-resonance imaging technology can be expensive both in time and cost. An alternative approach uses prediction models based on correlations among external defect indicators and internal defect features. Using external defect feature measurements, a prediction of internal defect size, shape, and depth can be generated. This paper examines the accuracy of the prediction models by comparing defect attributes on actual sawn board faces with the predicted defect attributes on virtual sawn boards. Although the models showed signifi cant correlations in the model-testing dataset for most defect types and features, they were never tested by the sawing of new samples and comparing predicted to actual defect attributes. Results for a test sample of 41 boards with 83 observed knot defects show that the prediction models can predict the occurrence of approximately 80 percent of all knot-type defects.

    Publication Notes

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    Citation

    Thomas, R. Edward. 2011. Validation of an internal hardwood log defect prediction model. In: Fei, Songlin; Lhotka, John M.; Stringer, Jeffrey W.; Gottschalk, Kurt W.; Miller, Gary W., eds. Proceedings, 17th central hardwood forest conference; 2010 April 5-7; Lexington, KY; Gen. Tech. Rep. NRS-P-78. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station: 77-82.

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