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.
