Predictive Modeling of the Engineered Wood Properties Using Genetic Algorithms with Distributed Data Fusion

Award Information
Agency:
Department of Agriculture
Branch
n/a
Amount:
$80,000.00
Award Year:
2005
Program:
SBIR
Phase:
Phase I
Contract:
2005-33610-15484
Award Id:
72496
Agency Tracking Number:
2005-00007
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
201 Center Park Drive, Suite 1140, Knoxville, TN, 37922
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
John Allen
(865) 218-1300
gannmann@earthlink.net
Business Contact:
John Allen
(865) 218-1300
gannmann@earthlink.net
Research Institution:
n/a
Abstract
Situation or Problem: The U.S. forest products industry contributed $406 billion to the U.S. economy and employed 2,140,399 people in 2002. Currently the forest products industry is facing unprecedented competition from international imports and high wood costs. In 2003, the engineered wood panel sector produced 64.3 billion square feet of panels of which wood waste ranged from 3% to 9%. Reducing wood waste by 1% can translate into annual savings of $500,000 to $700,000 per producer and save 1.9 to 5.9 billion square feet of wood. Two of the largest contributors to wood waste in engineered wood manufacture are rejected panels and high density targets. Rejected panels lead to rework and high density targets result from excessive process variation. High levels of wood waste lead to poor wood yield, and subsequently higher resin and energy use. Reducing wood waste and improving wood yield can help this important economic sector improve and sustain competitiveness. Indirect benefits to society from wiser use of the forest resource are immeasurable. Purpose: This Phase I project will address the problems of wood waste and poor wood yield in engineered wood manufacture by developing a real-time prediction system for physical properties using a hybrid Genetic Algorithm/Neural Network (GANN) with distributed data fusion.

* information listed above is at the time of submission.

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