Integration of Possibility-Based Optimization and Robust Design for Epistemic Uncertainty
Integration of Possibility-Based Optimization and Robust Design for Epistemic Uncertainty
复制标题
DOI：
10.1115/1.2717232
复制
发表时间：
2007-08
影响因子：
3.3
通讯作者：
B. Youn;K. Choi;Liu Du;D. Gorsich
中科院分区：
文献类型：
--
作者：
B. Youn;K. Choi;Liu Du;D. Gorsich
在实际工程应用中，存在两种不同类型的不确定性：偶然不确定性和认知不确定性。本研究试图开发一种具有认知不确定性的稳健设计优化。对于认知不确定性，基于可能性的设计优化可以提高故障率，而稳健的设计优化可以最大限度地减少产品质量损失。一般来说，产品质量损失是使用偶然不确定性的前两个统计矩来描述的：平均值和标准差。然而，当存在认知不确定性时，没有定义产品质量损失的衡量标准。本文首先提出了一种新的衡量具有认知不确定性的产品质量损失的指标，然后提出了基于可能性的鲁棒设计优化。为了数值效率和稳定性，采用丰富的性能测量方法进行基于可能性的鲁棒设计优化，并使用最大可能性搜索进行可能性分析。基于可能性的稳健设计优化考虑了三种不同类型的稳健目标：越小越好型（S 型）、越大越好型（L 型）和名义越好型（N 型）。使用示例来证明基于可能性的鲁棒设计优化的有效性，使用所提出的度量来衡量具有认知不确定性的产品质量损失。
In practical engineering applications, there exist two different types of uncertainties: aleatory and epistemic uncertainties. This study attempts to develop a robust design optimization with epistemic uncertainty. For epistemic uncertainties, a possibility-based design optimization improves the failure rate, while a robust design optimization minimizes the product quality loss. In general, product quality loss is described using the first two statistical moments for aleatory uncertainty: mean and standard deviation. However, there is no metric for product quality loss defined when having epistemic uncertainty. This paper first proposes a new metric for product quality loss with epistemic uncertainty, and then a possibility-based robust design optimization. For numerical efficiency and stability, an enriched performance measure approach is employed for possibility-based robust design optimization, and the maximal possibility search is used for a possibility analysis. Three different types of robust objectives are considered for possibility-based robust design optimization: smaller-the-better type (S-Type), larger-the-better type (L-Type), and nominal-the-better type (N-Type). Examples are used to demonstrate the effectiveness of possibility-based robust design optimization using the proposed metric for product quality loss with epistemic uncertainty.