This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Data Mining Based Feasible Domain Recognition for Automotive Structural Optimization
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 05, 2016 by SAE International in United States
Annotation ability available
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.
CitationYang, 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.
- MARK DOGGETT A., "Root Cause Analysis: A Framework for Tool Selection", QMJ VOL. 12, NO. 4/© 2005, ASQ.
- Wang K., Salhi A., Fraga E.S., "Process design optimisation using embedded hybrid visualization and data analysis techniques within a genetic algorithm optimisation framework", Chemical Engineering and Processing 43 (2004) 663-675, doi:10.1016/j.cep.2003.01.001.
- Bates R. A. and Wynn H. P., "Modelling Feasible Design Regions Using Lattice-based Kernel Methods", QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL Qual. Reliab. Engng. Int.2004;20:135-142, doi:10.1002/qre.624.
- Chen T, Huang J, "An efficient and practical approach to get a better optimum solution for structural optimization". Engineering Optimization (To be appears).
- Steinbach M., Yu H., Fang G., Kumar V. "Using constraints to generate and explore higher order discriminative patterns". Advances in Knowledge Discovery and Data Mining: 15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings, Part I.
- Shi L., Fu Y., Yang R.J., Wang B.P., Zhu P., "Selection of initial designs for multi-objective optimization using classification and regression tree", Struct Multidisc Optim, 2013 48:1057-1073, doi: 10.1007/s00158-013-0947-0.
- Quinlan, J. R. "Induction of Decision Trees". Mach. Learn. 1, 1 1986, 81-106
- Leo, B., Friedman, J.H., Olshen, R.A., Stone, C.J. "Stone classification and regression trees", Chapman&Hall, 1993.
- Fang, K.T., Shui, W.C, Pan, J.X., "Uniform design based on Latin squares" .Statistics Sinica,1999,9.905-912 .
- ESTECO Corporation. http://www.esteco.com/modefrontier.
- NCAC, "Development and Validation of a Finite Element Model for a 2001 Ford Taurus Passenger Sedan NCAC 2008-T-005", prepared for FHWA, Dec 2008.
- National Crash Analysis Center (NCAC), Public Finite Element Model Archive, 2001. http://www.ncac.gwu.edu/vml/models.html.
- Zhan, Z., Fu, Y., and Yang, R., "On Stochastic Model Interpolation and Extrapolation Methods for Vehicle Design," SAE Int. J. Mater. Manf. 6(3):517-531, 2013, doi:10.4271/2013-01-1386.
- Konak, A., Coit, D.W., Smith, A.E., "Multi-objective optimization using genetic algorithms, A tutorial", Reliability Engineering and System Safety 91 (2006) 992-1007.