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.
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ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
August 21–24, 2016
Charlotte, North Carolina, USA
Conference Sponsors:
- Design Engineering Division
- Computers and Information in Engineering Division
ISBN:
978-0-7918-5019-0
PROCEEDINGS PAPER
iFEED: Interactive Feature Extraction for Engineering Design
Hyunseung Bang,
Hyunseung Bang
Cornell University, Ithaca, NY
Search for other works by this author on:
Daniel Selva
Daniel Selva
Cornell University, Ithaca, NY
Search for other works by this author on:
Hyunseung Bang
Cornell University, Ithaca, NY
Daniel Selva
Cornell University, Ithaca, NY
Paper No:
DETC2016-60077, V007T06A037; 11 pages
Published Online:
December 5, 2016
Citation
Bang, H, & Selva, D. "iFEED: Interactive Feature Extraction for Engineering Design." Proceedings of the 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. V007T06A037. ASME. https://doi.org/10.1115/DETC2016-60077
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