Research Initiation Award: System Reduction Strategies for Efficient Design Synthesis

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The CE design approach requires the interaction of all engineering, production, and manufacturing groups simultaneously so as to reduce cost and bring higher quality products to market faster. One dilemma that makes CE difficult to practically implement in an industrial setting is the excessive computational requirements due to the inordinate number of participating groups. The number of 'communication paths' amongst these groups (which can be considered as 'couplings') is so large that formal optimization methods are impossible to apply. Sensitivity-based strategies that reduce the number of required communication paths amongst participating design groups, without sacrificing solution accuracy, are researched. These strategies are augmented by AI techniques to allow for human interface capabilities. A redesign of the design process itself is so as to facilitate concurrent engineering implementation. Some of the research issues addressed include: quantification of coupling strengths between subsystems, identification of candidate couplings for suspension or elimination, and optimal convergence schemes. United States automotive, aerospace, computer and electronics industries, among others, have been forced to respond to competitive pressures that have resulted in crippling losses of market share due to extensive product development times and inordinate costs. These industries have recognized the necessity of adopting the Concurrent Engineering (CE) design approach in order to create higher quality products that can be brought to market for significantly lower cost and time. The objective of this research is to develop AI-augmented strategies for increasing the efficiency of the design process for large scale, complex engineering systems, such as are encountered in Concurrent Engineering (CE) applications. Complex engineering systems are characterized by inherent couplings amongst the participating design groups, which makes traditional design and optimization approaches impractical.