An Improved K-Means Based Design Domain Recognition Method for Automotive Structural Optimization Chongqing University

Format:

Conference/Event

Author/Creator:

Hu, Hu, author.

Contributor:

Dong, Kuo

Xu, Wei

Zhan, Zhenfei

Zhao, Qingjiang

Conference Name:

WCX World Congress Experience (2018-04-10 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2018

Summary:

AbstractDesign optimization methods are widely used for weight reduction subjecting to multiple constraints in automotive industry. One of the major challenges is to search for the optimal design in an efficient manner. For complex design and optimization problems such as automotive applications, optimization algorithms work better if the initial searching points are within or close to feasible domains. In this paper, the k-means clustering algorithm is exploited to identify sets of reduced feasible domains from the original design space. Within the reduced feasible domains, the optimal design can be obtained efficiently. A mathematical example and a vehicle body structure design problem are used to demonstrate the effectiveness of the proposed method

Notes:

Vendor supplied data

Publisher Number:

2018-01-1032

Access Restriction:

Restricted for use by site license