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

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.