Alloys-by-Design Strategies Using Stochastic Multi-Objective Optimization
Abstract
The objective was to develop and demonstrate a technique for multi-objective optimization of the chemical composition of steel alloys with the use of an existing experimental database. The technique involves organization and execution of an iterative optimization experiment, which results in a set of Pareto optimum chemical compositions. The algorithms of response surface building known as IOSO was used where the response surfaces are built in accordance with existing experimental information. In a set of experiments the information on alloy properties in Pareto set neighborhood is accumulated, which makes it possible to increase the accuracy of results obtained. After each iteration of this technique, a set of new alloy compositions is formed which are assumed to be Pareto optimal, and for which experiment evaluation of properties should be carried out. For this work, algorithms of artificial neural networks were used that utilized radial-basis functions modified in order to build the response surfaces. The modifications consisted in the selection of ANN parameters at the stage of their training that are based on two criteria: minimal curvature of response surface, and provision of the best predictive properties for given subset of test points. The procedure was demonstrated to work successful and efficient
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 31, 2003
- Accession Number
- ADA424471
Entities
People
- George S. Dulikravich
Organizations
- University of Texas at Arlington