Rapid Residual Stress and Distortion Prediction in Cast Aluminum Components Using Artificial Neural Network and Part Geometry Characteristics Southeast University

Format:

Conference/Event

Author/Creator:

Quan, Quan, author.

Contributor:

Gao, Zhiqiang

Wang, Qigui

Wang, Yucong

Wen, Xiao

Xiao, Bowang

Conference Name:

SAE 2014 World Congress & Exhibition (2014-04-08 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2014

Summary:

AbstractHeat treated cast aluminum components like engine blocks and cylinder heads can develop significant amount of residual stress and distortion particularly with water quench. To incorporate the influence of residual stress and distortion in cast aluminum product design, a rapid simulation approach has been developed based on artificial neural network and component geometry characteristics. Multilayer feed-forward artificial neural network (ANN) models were trained and verified using FEA residual stress and distortion predictions together with part geometry information such as curvature, maximum dihedral angle, topologic features including node's neighbors, as well as quench parameters like quench temperature and quench media

Notes:

Vendor supplied data

Publisher Number:

2014-01-0755

Access Restriction:

Restricted for use by site license