Data Mining Based Feasible Domain Recognition for Automotive Structural Optimization Chongqing University

Format:

Conference/Event

Author/Creator:

Yang, Yang, author.

Contributor:

Jiang, Yazhou

Li, Jie

Yu, Helen

Zhan, Zhenfei

Zhao, Hui

Zheng, Ling

Conference Name:

SAE 2016 World Congress and Exhibition (2016-04-12 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2016

Summary:

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

Notes:

Vendor supplied data

Publisher Number:

2016-01-0268

Access Restriction:

Restricted for use by site license