Bayesian network classifiers for set-based collaborative design
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For many products, the design process is a complex system involving the interaction of many distributed design activities that need to be carefully coordinated. This research develops a new tool, called a Bayesian network classifier, to improve one specific aspect of this challenge: quantitatively capturing a consensus of which designs are feasible options for meeting system-wide engineering requirements. Classifiers enable designers to independently develop and share maps of the feasible regions of their design space, enabling set-based collaborative design. The method is set-based in that resources are used to thoroughly understand design tradeoffs before commitment is made to a final design. The method is collaborative because the maps are coordinated between design teams to represent the mutually feasible design space of all stake-holders. The benefits are a more thorough understanding of the system-wide design problem across team boundaries as well as knowledge capture for future re-use, potentially leading to faster product development and higher quality products.
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