Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1585875 | Materials Science and Engineering: A | 2006 | 11 Pages |
Abstract
Modification of the architecture of the artificial neural network is done to accommodate the information available from the knowledge base in the field of materials science for thermomechanically processed HSLA steel. The complicated architectures of these networks are made to satisfy the well-understood physical metallurgy principles, which administer the property response to the combined actions of the compositional and process parameters. The networks developed have been found to give very good convergence during training. The number of epochs required to reach the targeted error was found less for these networks than the conventional networks.
Related Topics
Physical Sciences and Engineering
Materials Science
Materials Science (General)
Authors
S. Datta, M.K. Banerjee,