Date of Award

9-15-2016

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Department of Operational Sciences

First Advisor

Kenneth W. Bauer, Jr., PhD.

Abstract

Robust parameter design (RPD) is used to identify a systems control settings that offer a compromise between obtaining desired mean responses and minimizing the variability about those responses. Two popular combined-array strategies the response surface model (RSM) approach and the emulator approach are limited when applied to simulations. In the former case, the mean and variance models can be inadequate due to a high level of non-linearity within many simulations. In the latter case, precise mean and variance approximations are developed at the expense of extensive Monte Carlo sampling. This research combines the RSM approach's efficiency with the emulator approach's accuracy. Non-linear metamodeling extensions, namely through Kriging and radial basis function neural networks, are made to the RSM approach. The mean and variance of second-order Taylor series approximations of these metamodels are generated via the Multivariate Delta Method and subsequent optimization problems employing these approximations are solved. Results show that improved prediction models can be attained through the proposed approach at a reduced computational cost. Additionally, a multi-response RPD problem solving technique based on desirability functions is presented to produce a solution that is mutually robust across all responses. Lastly, quality measures are developed to provide a holistic assessment of several competing RPD strategies.

AFIT Designator

AFIT-ENS-DS-16-S-026

DTIC Accession Number

AD1017985

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