Designing high strength multi-phase steel for improved strength-ductility balance using neural networks and multi-objective genetic algorithms

A1 Journal article (refereed)


Internal Authors/Editors


Publication Details

List of Authors: Datta S, Pettersson F, Ganguly S, Saxen H, Chakraborti N
Publisher: IRON STEEL INST JAPAN KEIDANREN KAIKAN
Publication year: 2007
Journal: Isij International
Journal acronym: ISIJ INT
Volume number: 47
Issue number: 8
Start page: 1195
End page: 1203
Number of pages: 9
ISSN: 0915-1559


Abstract

The properties of steels depend in a complex way on their composition and heat treatment and neural networks have therefore recently been widely used for capturing these relationships. Two different methods of reducing the network connectivity, viz a pruning algorithm and a multi-objective predator prey genetic algorithm, have been used for neural network modeling of the mechanical properties of high strength steels, so that relevant connections within the networks are revealed. This provides important understanding on the variables and their relationship with mechanical properties, In the pruning algorithm the lower layer of the network is gradually reduced by removing less significant connections. In the predator prey algorithm, a genetic algorithm based multi-objective optimization technique is used to train the neural network and a Pareto front is developed by minimizing the training error along with the network size. The results of both techniques reveal that they can extract more knowledge from the data, which is difficult to obtain from conventional neural models. The relative relevance of the composition and processing parameters detected could be used for designing steel with tailored property balance. The results developed by the two techniques are also found to be comparable.


Keywords

alloy design, genetic algorithm, high strength multiphase steel, multi-objective optimization, neural network model, predator prey algorithm, pruning algorithm

Last updated on 2019-22-11 at 05:03