Evolutionary design of nickel-based superalloys using data-driven genetic algorithms and related strategies

A1 Journal article (refereed)


Internal Authors/Editors


Publication Details

List of Authors: R. Jha, F. Pettersson, G. S. Dulikravich, H. Saxen, N. Chakraborti
Publisher: TAYLOR & FRANCIS INC
Publication year: 2015
Journal: Materials and Manufacturing Processes
Journal acronym: MATER MANUF PROCESS
Volume number: 30
Issue number: 4
Start page: 488
End page: 510
Number of pages: 23
ISSN: 1042-6914
eISSN: 1532-2475


Abstract

Data-driven models were constructed for the mechanical properties of multi-component Ni-based superalloys, based on systematically planned, limited experimental data using a number of evolutionary approaches. Novel alloy design was carried out by optimizing two conflicting requirements of maximizing tensile stress and time-to-rupture using a genetic algorithm-based multi-objective optimization method. The procedure resulted in a number of optimized alloys having superior properties. The results were corroborated by a rigorous thermodynamic analysis and the alloys found were further classified in terms of their expected levels of hardenabilty, creep, and corrosion resistances along with the two original objectives that were optimized. A number of hitherto unknown alloys with potential superior properties in terms of all the attributes ultimately emerged through these analyses. This work is focused on providing the experimentalists with linear correlations among the design variables and between the design variables and the desired properties, non-linear correlations (qualitative) between the design variables and the desired properties, and a quantitative measure of the effect of design variables on the desired properties. Pareto-optimized predictions obtained from various data-driven approaches were screened for thermodynamic equilibrium. The results were further classified for additional properties.


Keywords

Alloy design, Data-driven modeling, Evolutionary optimization, Genetic algorithms, Genetic programming, Meta-models, Multi-objective optimization, Phase equilibria, Superalloy

Last updated on 2019-15-11 at 05:31