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

NSF 青年研究员：解释多学科设计和制造优化中的不确定性

• 批准号: 9457179
• 负责人: John Renaud
• 金额: $ 33.25万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-09-01 至 2000-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9457179&HistoricalAwards=false
• 关键词: NSF; Young; Investigator; Accounting; Uncertainty

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.

小行星9457179 现有的多学科设计优化（MDO）技术一般不考虑系统设计和制造的差异。 在这项研究中，稳健的优化设计策略（即，基于田口的）将嵌入到MDO算法目前正在开发，利用人工神经网络和系统近似的全局灵敏度。 作为这项工作的一部分，将进行人工神经网络建模设计和制造的不确定性的效用的调查。 基于目前的研究人工神经网络的设计空间映射，它被认为是人工神经网络可以提供一个理想的机制，在整个产品的生命周期设计的方差建模。 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，必须开发新技术，帮助设计人员在整个产品设计周期中作出决定。 在这项研究中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。