An integrated framework for optimization under uncertainty using inverse Reliability strategy
An integrated framework for optimization under uncertainty using inverse Reliability strategy
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DOI：
10.1115/1.1759358
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发表时间：
2004-07
影响因子：
3.3
通讯作者：
Xiaoping Du;A. Sudjianto;Wei Chen-
中科院分区：
文献类型：
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作者：
Xiaoping Du;A. Sudjianto;Wei Chen-
在这项工作中，我们提出了一个综合框架下的不确定性，可以同时考虑到设计目标的鲁棒性和概率设计约束的优化。这项工作的基本发展是就业的逆可靠性策略，使用百分位性能评估的目标鲁棒性和概率约束。客观稳健性的百分位数公式为我们提供了一个准确的评估的变化的客观性能和概率测量的鲁棒性。与田口提出的传统方法相比，我们可以获得更合理的复合噪声组合，以实现稳健设计目标。所提出的配方是非常有效的解决，因为它只需要评估所需的可靠性水平的约束函数。这项工作的其他主要发展是一个新的搜索算法的逆可靠性（MPPIR），可用于有效地评估百分位性能的鲁棒性和可靠性评估的最可能点。在MPPIR搜索中采用多种策略，包括使用最陡上升方向和弧搜索。该算法适用于任意连续分布随机变量的一般非凹和非凸性能函数。通过算例验证了MPPIR搜索算法的有效性。最后，以某车用内燃机活塞的稳健性与可靠性综合设计为例，说明了所提方法的优越性。
In this work, we propose an integrated framework for optimization under uncertainty that can bring both the design objective robustness and the probabilistic design constraints into account. The fundamental development of this work is the employment of an inverse reliability strategy that uses percentile performance for assessing both the objective robustness and probabilistic constraints. The percentile formulation for objective robustness provides us an accurate evaluation of the variation of an objective performance and a probabilistic measurement of the robustness. We can obtain more reasonable compound noise combinations for a robust design objective compared to using the traditional approach proposed by Taguchi. The proposed formulation is very efficient to solve since it only needs to evaluate the constraint functions at the required reliability levels. The other major development of this work is a new search algorithm for the Most Probable Point of Inverse Reliability (MPPIR) that can be used to efficiently evaluate percentile performances for both robustness and reliability assessments. Multiple strategies are employed in the MPPIR search, including using the steepest ascent direction and an arc search. The algorithm is applicable to general non-concave and non-convex performance functions of random variables following any continuous distributions. The effectiveness of the MPPIR search algorithm is verified using example problems. Overall, an engineering example on integrated robust and reliability design of a vehicle combustion engine piston is used to illustrate the benefits of our proposed method.