A Bayesian Treatment of Uncertainty in Simulation-Based Methods for Enhancing Process and Product Robustness

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ABSTRACTA Bayesian Treatment of Uncertainty in Simulation-Based Methods for Enhancing Process and Product Robustness This grant provides funding to create a Bayesian methodology for managing various forms of uncertainty when using simulation-based methods for enhancing the robustness of manufacturing processes and manufactured products. Three types of uncertainty that will be treated are parameter uncertainty, simulation uncertainty, and model uncertainty. An example of parameter uncertainty is variation in material properties in stamping processes. Simulation uncertainty results from limitations on the number of input variable combinations at which one may conduct computationally expensive simulation runs. Model uncertainty results from differences between the simulation output and the physical world. A Bayesian framework will be used to quantitatively represent the effects of all three forms of uncertainty, in terms of their impact on the robust design objective. This objective-oriented representation will form the basis for a Bayesian methodology for supporting critical decision making, such as guiding the simulation and physical experiments to provide the greatest information for optimizing the design, deciding whether current information is sufficient to terminate simulation and confidently optimize the design, and ensuring that the design solution truly results in robustness to all three forms of uncertainty.Robust design based on physical experimentation is firmly established practice. However, to reduce development cycle time or when physical experiments are impractical, computer simulations are increasingly important replacements for or supplements to physical experimentation. If successful, the results of this research will provide much needed tools for efficiently achieving robust design optimization based on computer simulation. Because this research is not restricted to a particular type of simulation code and allows the user to choose from a variety of probabilistic objective functions, with or without constraints, it is expected to find widespread application. Development of easy-to-interpret graphical displays for visualizing the analytical results will facilitate implementation of the methodology, broadening its expected impact.