Data Mining Based Feasible Domain Recognition for Automotive Structural Optimization

Paper #:
  • 2016-01-0268

Published:
  • 2016-04-05
DOI:
  • 10.4271/2016-01-0268
Citation:
Yang, J., Zhan, Z., Zheng, L., Yu, H. et al., "Data Mining Based Feasible Domain Recognition for Automotive Structural Optimization," SAE Technical Paper 2016-01-0268, 2016, https://doi.org/10.4271/2016-01-0268.
Pages:
7
Abstract:
Computer modeling and simulation have significantly facilitated the efficiency of product design and development in modern engineering, especially in the automotive industry. For the design and optimization of car models, optimization algorithms usually work better if the initial searching points are within or close to a feasible domain. Therefore, finding a feasible design domain in advance is beneficial. A data mining technique, Iterative Dichotomizer 3 (ID3), is exploited in this paper to identify sets of reduced feasible design domains from the original design space. Within the reduced feasible domains, optimal designs can be efficiently obtained while releasing computational burden in iterations. A mathematical example is used to illustrate the proposed method. Then an industrial application about automotive structural optimization is employed to demonstrate the proposed methodology. The results show the proposed method’s potential in practical engineering.
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