NDLI: Optimizing Casting Parameters of Ingot Based on Neural Network and Genetic Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Pei Zhang Zhiqiang Xu Fengshan Du
Copyright Year	2008
Description	Author affiliation: Mech. Eng. Dept., YanShan Univ., Qinhuangdao (Pei Zhang; Zhiqiang Xu; Fengshan Du)
Abstract	By integrating the predominances of neural network (NN) and genetic algorithm (GA), it takes aid at heavy ingot casting flaw to optimize the foundry technique parameters. Under the matching schemes of foundry technique parameters that gained with uniform design method, the casting process of ingot was simulated by finite element method (FEM). A neural network was set up to reflect the influence of foundry technique parameter on casting flaw; every scheme is taken as the training sample or test sample. A program was made to combine NN with GA. After 300 generations of GA, the solution is stable. With the optimal casting parameters, the casting flaws are reduced and less than any sample results.
Starting Page	545
Ending Page	548
File Size	599144
Page Count	4
File Format	PDF
ISBN	9780769533049
DOI	10.1109/ICNC.2008.707
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	casting flaw Temperature Design methodology Artificial neural networks Steel Finite element methods neural network Genetic algorithms shrinkage pore genetic algorithm Casting Neural networks Foundries Testing
Content Type	Text
Resource Type	Article

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