Abstract: 

Xiao Tang and S. Krishnamurty, “Performance Estimation and Robust Design Decisions,”Design EngineeringTechnical Conference ’2000 ,DAC-14239

Decision based engineering design research community addresses the notion of design as a decision making process. The quintessence of the decision based design approaches is to first model the design situation that results in a mapping from the design option space to the performance attribute space, and then to obtain the designer’s preference information regarding the tradeoffs among the attributes in order to construct a system utility function on the attributes representing the overall desirability of any combination of alternatives. It can be argued from this perspective that the essence of design decision-making lies in understanding the impact of uncertainty on utility. This paper deals with two major issues critical to the development and implementation of a decision-based robust design, namely, representation of design performance under conditions of uncertainty and the development of a robust design decision model.
In this paper, a reliability-based robust optimal design decision model is presented to handle a primary source of uncertainty associated with performance estimation, namely, the consequence of the unpredictability of the model parameters or variables. Specifically, this paper presents a computationally efficient procedure for accurate estimation of performance variance using a novel Surround Point Method (SPM) and discusses its incorporation into a decision-based robust design framework. Using Monte Carlo simulation (MCS) results in constructing an optimal design of outer array in a design of experiments (DOE) setup, this approach aims to mimic MCS-like results throughout the design space without requiring the need to run computationally intensive simulations at each design instance. Its application in design optimization eliminates the need for surrogate response surface models, which can be lead to significant errors in variance estimation. Preliminary results appear to indicate that the SPM-based uncertainty estimation method can offer the best promise in achieving an optimal balance between computational complexity and design-scenario independence. As such, it can be expected to be a viable and applicable probability estimation tool in generic engineering design, and particularly useful in highly nonlinear configuration design with many design variables. Towards its implementation in a design scenario, this paper explicitly introduces the concept of robustness criteria and the development of quality utility for use in design decisions. These issues are discussed in the context of engineering design decision-making with the aid of two case studies and the results are discussed.

Keywords: Engineering Design, Robust Design, Design of Experiments, Expected Utility Theory

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