In this study, the material utilization efficiency in terms of cutting yield for two furniture frame models from a local upholstery company was evaluated using computer simulation software with optimization capacity. Effects of full panel size of wood-based composites, furniture frame number, and frame part shape and size on cutting yield were investigated. Simulation results indicate that the cutting yield is affected by full panel sizes and number of frames cut. Curve-shaped parts and small size parts can lower the material cutting yield. The overall material cutting yield obtained with simulation software can reach the range from 80 to 84%, which was higher than the current yield of 79.80% from the company. Optimum cutting yield of 84% can be achieved when 40 frames were produced with 4.5 by 8 feet panel.

This thesis describes a computing environment WBCSim and its experiment management component. WBCSim is a web-based simulation system used to increase the productivity of wood scientists conducting research on wood-based composite and material manufacturing processes. This experiment management component integrates a web-based graphical front end, server scripts, and a database management system to allow scientists to easily save, retrieve, and perform customized operations on experimental data. A detailed description of the system architecture and the experiment management component is presented, along with a typical scenario of usage. / Master of Science

A problem solving environment (PSE) is a computational system that provides a complete and convenient set of high level tools for solving problems from a specific domain. This thesis takes an in-depth look at the experiment management aspect of PSEs, which can be divided into three levels: 1) data management, 2) change management, and 3) execution management. At the data management level, anything related to an experiment (computer simulation) should be stored and documented. A database management system can be used to store the simulation runs for a PSE. Then various high level interfaces can be provided to allow users to save, retrieve, search, and compare these simulation runs. At the change management level, a scientist should only focus on how to solve a problem in the experiment domain. Aside from running experiments, a scientist may only consider how to define a new model, how to modify an existing model, and how to interpret an experiment result. By using XML to describe a simulation model and unify various implementation layers, changing an existing model in a PSE can be intuitive and fast. At the execution management level, how an experiment is executed is the main concern. By providing a computational steering capability, a scientist can pause, examine, and compare the intermediate results from a simulation. Contrasted with the traditional way of running a lengthy simulation to see the result at the end, computational steering can leverage the user's expert knowledge on the fly (during the simulation run) and provide new insights and new product design opportunities. This thesis illustrates these concepts and implementation by using WBCSim as an example. WBCSim is a PSE that increases the productivity of wood scientists conducting research on wood-based composite materials and manufacturing processes. It integrates Fortran 90 simulation codes with a Web based graphical front end, an optimization tool, and various visualization tools. The WBCSim project was begun in 1997 with support from United States Department of Agriculture, Department of Energy, and Virginia Tech. It has since been used by students in several wood science classes, by graduate students and faculty, and by researchers at several forest products companies. WBCSim also serves as a test bed for the design, construction, and evaluation of useful, production quality PSEs. / Ph. D.

Large scale, multidisciplinary, engineering designs are always difficult due to the complexity and dimensionality of these problems. Direct coupling between the analysis codes and the optimization routines can be prohibitively time consuming. One way of tackling this problem is by constructing computationally cheap(er) approximations of the expensive simulations, that mimic the behavior of the simulation model as closely as possible. This paper presents a data driven, surrogate based optimization algorithm that uses a trust region based sequential approximate optimization (SAO) framework and a statistical sampling approach based on design of experiment (DOE) arrays. The algorithm is implemented using techniques from the two packages SURFPACK and SHEPPACK that provide a collection of approximation algorithms to build the surrogates and three different DOE techniques: full factorial (FF), Latin hypercube sampling (LHS), and central composite design (CCD) are used to train the surrogates. The biggest concern in using the proposed methodology is the generation of the required database. This thesis proposes a data driven approach where an expensive simulation run is required if and only if a nearby data point does not exist in the cumulatively growing database. Over time the database matures and is enriched as more and more optimizations are performed. Results show that the response surface approximations constructed using design of experiments can be effectively managed by a SAO framework based on a trust region strategy. An interesting result is the significant reduction in the number of simulations for the subsequent runs of the optimization algorithm with a cumulatively growing simulation database. / Master of Science