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. The proposed system will lower the rate of rejected panels, optimize throughput, identify key process parameters, optimize wood usage, promote lower resin use, lower energy use and improve wood yield. OBJECTIVES: The goal of Phase I is to provide proof-of-principle for the concept of real-time prediction of the physical properties of engineered wood using a GANN with distributed data fusion. The proof-of-principle will demonstrate implementation of this technique for medium density fiberboard (MDF) manufacture, by accurately predicting the real-time internal bond (IB) of MDF. The system will also be designed to be transportable to all types of engineered wood and other wood properties. There are three technical questions related to the research objectives. 1. Development of an automated real-time distributed data fusion system. -- The automated fusion of diverse types of real-time sensor data with event-based destructive tests will advance information technology, automated data fusion and associated issues. 2. Development of a real-time Genetic Algorithm to predict the physical properties of engineered wood. This objective will advance the mathematical and wood sciences by determining the real-time sources of process variation that influence physical properties. Critical technical questions will be addressed in defining NN-based genetic representation and genetic operations to predict the value of a single material property from parameter values acquired during real-time data acquisition from a GA-optimized number of sensors. 3. Development of an information software platform for industrial use. Ease-of-use of an object oriented program and efficiency in processing information across an industrial LAN will be the critical computational science question addressed by this objective. APPROACH: 1. Development of an automated real-time distributed data fusion system: Real-time process data will be aligned with the internal bond strength of medium density fiberboard (MDF) at one manufacturing test site. Lag times, corresponding to the period of time required for the furnish to travel through the process from the point where a given parameter has an influence, to the point where the panel is extracted for destructive testing will be estimated and included in the system. Statistical estimates will be derived for the real-time data and exclude null fields, data type error, string data type, time overlap data and statistical outliers. A unique number will be generated when the panel is extracted from the process, and will be later used to match process data with lab results. When the lab results are matched with the process data, this combined data will be recorded in an automated relational database. 2. Development of a real-time Genetic Algorithm/Neural Network system for prediction of the physical properties of engineered wood: Three principal tasks comprise the GANN portion of the proposed work: a) Incorporate Automated Input Parameter Selection: A typical composite production line may include sensors for monitoring the values of several hundred to more than one thousand process variables. Not all of these are of relevance to the prediction of a given material property. The selection task is one especially well suited to solution by GA methods. b) Add Automated Network Pruning: One of the hazards in almost any application of neural network methods is inadvertent production of networks of excessive size. In our GANN systems, it is possible to adjust fitness measures to favor networks that are no larger than necessary to perform the predictive tasks required of them. A scheme in which an estimate of generalizing capability (as opposed to mere mimicry) will be incorporated directly into one of the fitness measures. c) Perform Extensive Network Validation Studies: Although our preliminary statistical validation results appear most promising, the GANN method will be accepted in industry only to the extent that it can be demonstrated to be statistically valid and robust. A believable demonstration of predictive performance can only be performed in the context of extensive and broad data spanning a plausible range of process parameter values, material property values, and measurement noise. It is our intention that a final report on the Phase I work will include extensive measures of GANN performance over a sufficiently broad spectrum of conditions such that others contemplating the use of the method will be able to make informed and careful judgments concerning potential applicability 3. Development of an information software platform for industrial use The Phase I GANN software platform for industrial will have three Visual Net components: a) Genetic Algorithm/Neural Network (GANN) Trainer 1.0, b) Genetic Algorithm/Neural Network (GANN) Processor 1.0, c) Genetic Algorithm/Neural Network (GANN) Client 1.0. The Visual Net software will have point and click functionality