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Towards real-time non-destructive lu...

Saravi, Albert.

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Towards real-time non-destructive lumber grading using X-ray images and modulus of elasticity signals.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Towards real-time non-destructive lumber grading using X-ray images and modulus of elasticity signals./
Author:	Saravi, Albert.
Description:	128 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 1038.
Contained By:	Dissertation Abstracts International66-02B.
Subject:	Engineering, Chemical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ99545
ISBN:	9780612995451

Towards real-time non-destructive lumber grading using X-ray images and modulus of elasticity signals.
Saravi, Albert.

Towards real-time non-destructive lumber grading using X-ray images and modulus of elasticity signals. - 128 p.

Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 1038.

Thesis (Ph.D.)--The University of British Columbia (Canada), 2005.

The objective of this study was to examine relationships between the physical properties of a board and the strength of a board for use in an intelligent lumber grading system. Available research literature and commercial grading systems, that have taken steps in this direction, are described in this study. Some previous work on estimating the strength of lumber has been based on mathematical models derived from different approximation methods such as regression, function approximation or neural networks. The disadvantage of this approach is that a large training set of board scans are required to be representative of the various species, harvesting sites, and geometrical variations between boards (grain direction, knot location and size, etc.). A major contribution of this thesis was to develop a mechanics based method of determining board strength that relies only upon simple in-mill measurement of X-Ray and Modulus of Elasticity (MOE) signals.

ISBN: 9780612995451Subjects--Topical Terms:

1018531
Engineering, Chemical.

Towards real-time non-destructive lumber grading using X-ray images and modulus of elasticity signals.
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100 1 $a Saravi, Albert. $3 1912515
245 1 0 $a Towards real-time non-destructive lumber grading using X-ray images and modulus of elasticity signals.
300 $a 128 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 1038.
500 $a Adviser: Peter D. Lawrence.
502 $a Thesis (Ph.D.)--The University of British Columbia (Canada), 2005.
520 $a The objective of this study was to examine relationships between the physical properties of a board and the strength of a board for use in an intelligent lumber grading system. Available research literature and commercial grading systems, that have taken steps in this direction, are described in this study. Some previous work on estimating the strength of lumber has been based on mathematical models derived from different approximation methods such as regression, function approximation or neural networks. The disadvantage of this approach is that a large training set of board scans are required to be representative of the various species, harvesting sites, and geometrical variations between boards (grain direction, knot location and size, etc.). A major contribution of this thesis was to develop a mechanics based method of determining board strength that relies only upon simple in-mill measurement of X-Ray and Modulus of Elasticity (MOE) signals.
520 $a In this thesis, an intelligent mechanics-based lumber grading system was developed to provide a better estimation of the strength of a board nondestructively. This system processed X-Ray-extracted geometric features (of 1080 boards that eventually underwent destructive strength testing) by using Finite Element Methods (FEM) to generate associated stress fields. In order to find a few significant mechanics-based features, the stress fields were then fed to a feature-extracting-processor which produced twenty six strength predicting features. The best strength predicting features were determined from the coefficient of determination (correlation r squared) between the features and actual strengths of the boards. The coefficient of determination of each feature (or combination of features) with the actual strength of the board were calculated and compared. A coefficient of determination of 0.42 was achieved by using a longitudinal (along the local grain angle) maximum stress concentration (MSC) feature to predict the estimated strength of lumber.
520 $a In addition to the above feature, a Weibull based feature was defined and examined. Since it is based on the whole stress field; whereas, maximum stress concentration based feature is based on one point in stress field, we hoped to get a better correlation. By implementing a system using the Weibull based feature, a coefficient of determination of 0.47 was achieved which is slightly higher than the MSC based feature.
520 $a Next, a combination of X-Ray and Modulus of Elasticity (MOE) signals were used to estimate the strength of lumber.
520 $a In the final step, the FEM processor was replaced by a knowledge-based system comprised of a fast table lookup for all FEM-modeled knot locations and sizes. To the extent that this table is representative of the important strength-reducing factors in a board, it replaces the need for a large training set of actual boards with real knots of differing sizes and locations. This knowledge-based system will permit a real-time board strength estimation system to be developed. We were able to fine-tune the mechanics based algorithm and reduce the FEM calculation errors. By implementing the knowledge based system coefficients of determination of 0.44, 0.46 and 0.67 were achieved for the MSC, the Weibull and combined (MOE and X-Ray) algorithm-based features respectively. (Abstract shortened by UMI.)
590 $a School code: 2500.
650 4 $a Engineering, Chemical. $3 1018531
650 4 $a Agriculture, Wood Technology. $3 1031154
690 $a 0542
690 $a 0746
710 2 0 $a The University of British Columbia (Canada). $3 626643
773 0 $t Dissertation Abstracts International $g 66-02B.
790 1 0 $a Lawrence, Peter D., $e advisor
790 $a 2500
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ99545