Data-mining driven reconfigurable product family design framework for aerodynamic particle separators

Conrad S. Tucker, Douglas E. Barker, Harrison M. Kim, Yuanhui Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a design framework that integrates data driven knowledge discovery with engineering design simulation to create an optimal product family of aerodynamic air particle separators. Two key data mining techniques are presented in this work to help guide the product design process. First is the RELIEFF attribute weighting criterion that identifies and ranks product attributes based on their importance within the raw data set. The second is the X-Means clustering approach that eliminates the need for the number of clusters to be stated a priori. The engineering product family optimization stage that follows will therefore be a true representation of the raw data set by attaining an optimal design based on the number of clusters generated by the X-Means clustering process while concurrently taking into account attribute weighting information. The resulting product portfolio will achieve cost savings and autonomous reconfiguration of product architecture under the notion of reconfigurable product family. A family of prototype aerodynamic air particle separators will be used to evaluate the final solution of the reconfigurable product family model generation.

Original languageEnglish (US)
Title of host publication12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO
StatePublished - Dec 1 2008
Event12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO - Victoria, BC, Canada
Duration: Sep 10 2008Sep 12 2008

Publication series

Name12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO

Other

Other12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO
CountryCanada
CityVictoria, BC
Period9/10/089/12/08

ASJC Scopus subject areas

  • Aerospace Engineering
  • Mechanical Engineering

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  • Cite this

    Tucker, C. S., Barker, D. E., Kim, H. M., & Zhang, Y. (2008). Data-mining driven reconfigurable product family design framework for aerodynamic particle separators. In 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO [2008-6065] (12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO).