Reliability-based robust design optimization (RBRDO) is a crucial tool for life-cycle quality improvement. Gaussian process (GP) model is an effective alternative modeling technique that is widely used in robust parameter design. However, there are few studies to deal with reliability-based design problems by using GP model. This article proposes a novel life-cycle RBRDO approach concerning response uncertainty under the framework of GP modeling technique. First, the hyperparameters of GP model are estimated by using the Gibbs sampling procedure. Second, the expected partial derivative expression is derived based on GP modeling technique. Moreover, a novel failure risk cost function is constructed to assess the life-cycle reliability. Then, the quality loss function and confidence interval are constructed by simulated outputs to evaluate the robustness of optimal settings and response uncertainty, respectively. Finally, an optimization model integrating failure risk cost function, quality loss function, and confidence interval analysis approach is constructed to find reasonable optimal input settings. Two case studies are given to illustrate the performance of the proposed approach. The results show that the proposed approach can make better trade-offs between the quality characteristics and reliability requirements by considering response uncertainty.

中文翻译：

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具有响应不确定性的GP模型基于生命周期可靠性的鲁棒设计优化

基于可靠性的稳健设计优化 (RBRDO) 是改善生命周期质量的重要工具。高斯过程 (GP) 模型是一种有效的替代建模技术，广泛用于稳健参数设计。然而，很少有研究使用GP模型来处理基于可靠性的设计问题。本文在GP建模技术的框架下提出了一种新的关于响应不确定性的生命周期RBRDO方法。首先，使用 Gibbs 采样程序估计 GP 模型的超参数。其次，基于GP建模技术推导出期望的偏导数表达式。此外，构建了一个新的故障风险成本函数来评估生命周期的可靠性。然后，质量损失函数和置信区间由模拟输出构建，分别用于评估最优设置和响应不确定性的稳健性。最后，构建集成故障风险成本函数、质量损失函数和置信区间分析方法的优化模型，以寻找合理的最优输入设置。给出了两个案例研究来说明所提出方法的性能。结果表明，所提出的方法可以通过考虑响应不确定性在质量特性和可靠性要求之间做出更好的权衡。并构建置信区间分析方法以找到合理的最佳输入设置。给出了两个案例研究来说明所提出方法的性能。结果表明，所提出的方法可以通过考虑响应不确定性在质量特性和可靠性要求之间做出更好的权衡。并构建置信区间分析方法以找到合理的最佳输入设置。给出了两个案例研究来说明所提出方法的性能。结果表明，所提出的方法可以通过考虑响应不确定性在质量特性和可靠性要求之间做出更好的权衡。