NSF Young Investigator: Accounting for Uncertainty in Multidisciplinary Design and Manufacturing Optimization

9457179更新现有的多学科设计优化技术一般不考虑系统设计和制造差异。在这项研究中，稳健的优化设计策略(即基于田口的)将被嵌入到目前正在开发的利用人工神经网络和全局灵敏度进行系统逼近的MDO算法中。作为这项工作的一部分，将对人工神经网络在建模、设计和制造不确定性方面的效用进行调查。基于人工神经网络用于设计空间映射的研究现状，认为人工神经网络可以提供一种理想的机制来建模整个产品生命周期的设计过程中的差异。为了使美国公司在先进的产品设计和制造方面保持竞争力，必须开发新的技术来帮助设计师在整个产品设计周期中做出决策。这项研究将开发的设计工具和方法将有助于确保提高美国在先进产品设计和制造方面的竞争力。

9457179 Renaud Existing multidisciplinary design optimization (MDO) technologies do not generally account for system design and manufacturing variances. In this research robust design strategies for optimization (i.e., Taguchi based) will be embedded in MDO algorithms currently being developed which make use of both artificial neural networks and global sensitivities for system approximation. As part of this effort an investigation of the utility of artificial neural networks for modeling design and manufacturing uncertainties will be undertaken. Based on current investigations of artificial neural networks for design space mapping, it is thought that artificial neural networks may provide an ideal mechanism for modeling variance throughout a product's life cycle design. In order for the United States companies to remain competitive in advanced product design and manufacture, new technologies to aid designers in making decisions throughout the product design cycle must be developed. The design tools and methodologies to be developed in this research will help ensure the advancement of United States competitiveness in advanced product design and manufacture.