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

贝叶斯处理不确定性的模拟为基础的方法，以提高过程和产品的鲁棒性该补助金提供资金，以创建一个贝叶斯方法，用于管理各种形式的不确定性时，使用模拟为基础的方法，以提高制造过程和制造产品的鲁棒性。将被处理的三种类型的不确定性是参数不确定性、模拟不确定性和模型不确定性。参数不确定性的一个例子是冲压过程中材料特性的变化。模拟的不确定性是由输入变量组合的数量限制引起的，在这些组合下，可以进行计算上昂贵的模拟运行。模型的不确定性是由模拟输出和物理世界之间的差异造成的。贝叶斯框架将用于定量表示所有三种形式的不确定性的影响，就其对稳健设计目标的影响而言。这种面向目标的表示将形成贝叶斯方法的基础，用于支持关键决策，例如指导模拟和物理实验以提供用于优化设计的最大信息，决定当前信息是否足以终止模拟并自信地优化设计，并确保设计解决方案真正对所有三种形式的不确定性具有鲁棒性。基于物理实验的鲁棒性设计是牢固确立的实践。然而，为了缩短开发周期或当物理实验不切实际时，计算机模拟越来越重要地替代或补充物理实验。如果成功，本研究的结果将提供急需的工具，有效地实现稳健的设计优化的基础上，计算机仿真。由于这项研究不限于特定类型的仿真代码，并允许用户从各种概率目标函数中进行选择，有或没有约束，它有望找到广泛的应用。开发易于解释的图形显示，使分析结果可视化，将有助于该方法的实施，扩大其预期影响。

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.