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

Paper #:
  • 2014-01-0755

Published:
  • 2014-04-01
DOI:
  • 10.4271/2014-01-0755
Citation:
Quan, Z., Gao, Z., Wang, Q., Wen, X. et al., "Rapid Residual Stress and Distortion Prediction in Cast Aluminum Components Using Artificial Neural Network and Part Geometry Characteristics," SAE Technical Paper 2014-01-0755, 2014, doi:10.4271/2014-01-0755.
Pages:
9
Abstract:
Heat 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.
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