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iFEED: Interactive Feature Extraction for Engineering Design

[+] Author Affiliations
Hyunseung Bang, Daniel Selva

Cornell University, Ithaca, NY

Paper No. DETC2016-60077, pp. V007T06A037; 11 pages
doi:10.1115/DETC2016-60077
From:
  • ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 7: 28th International Conference on Design Theory and Methodology
  • Charlotte, North Carolina, USA, August 21–24, 2016
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5019-0
  • Copyright © 2016 by ASME

abstract

One of the major challenges faced by the decision maker in the design of complex engineering systems is information overload. When the size and dimensionality of the data exceeds a certain level, a designer may become overwhelmed and no longer be able to perceive and analyze the underlying dynamics of the design problem at hand, which can result in premature or poor design selection. There exist various knowledge discovery and visual analytic tools designed to relieve the information overload, such as BrickViz, Cloud Visualization, ATSV, and LIVE, to name a few. However, most of them do not explicitly support the discovery of key knowledge about the mapping between the design space and the objective space, such as the set of high-level design features that drive most of the trade-offs between objectives. In this paper, we introduce a new interactive method, called iFEED, that supports the designer in the process of high-level knowledge discovery in a large, multiobjective design space. The primary goal of the method is to iteratively mine the design space dataset for driving features, i.e., combinations of design variables that appear to consistently drive designs towards specific target regions in the design space set by the user. This is implemented using a data mining algorithm that mines interesting patterns in the form of association rules. The extracted patterns are then used to build a surrogate classification model based on a decision tree that predicts whether a design is likely to be located in the target region of the tradespace or not. Higher level features will generate more compact classification trees while improving classification accuracy. If the mined features are not satisfactory, the user can go back to the first step and extract higher level features. Such iterative process helps the user to gain insights and build a mental model of how design variables are mapped into objective values. A controlled experiment with human subjects is designed to test the effectiveness of the proposed method. A preliminary result from the pilot experiment is presented.

Copyright © 2016 by ASME

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