Evolutionary Design of Nickel-Based Superalloys Using Data-Driven Genetic Algorithms and Related Strategies

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Jha:2015:MMP,
  author =       "R. Jha and F. Pettersson and G. S. Dulikravich and 
                 H. Saxen and N. Chakrabortic",
  title =        "Evolutionary Design of Nickel-Based Superalloys Using
                 Data-Driven Genetic Algorithms and Related Strategies",
  journal =      "Materials and Manufacturing Processes",
  year =         "2015",
  volume =       "30",
  number =       "4",
  pages =        "488--510",
  month =        apr,
  email =        "rjha001@fiu.edu",
  keywords =     "genetic algorithms, genetic programming, Alloy design,
                 Data-driven modelling, Evolutionary optimisation,
                 Genetic algorithms, Genetic programming, Meta-models,
                 Multi-objective optimisation, Phase equilibria,
                 Superalloy",
  ISSN =         "1042-6914",
  URL =          "http://dx.doi.org/10.1080/10426914.2014.984203",
  DOI =          "doi:10.1080/10426914.2014.984203",
  size =         "23 pages",
  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 optimising two
                 conflicting requirements of maximising tensile stress
                 and time-to-rupture using a genetic algorithm-based
                 multi-objective optimization method. The procedure
                 resulted in a number of optimised 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 optimised. 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-optimised predictions obtained from various
                 data-driven approaches were screened for thermodynamic
                 equilibrium. The results were further classified for
                 additional properties.",
  notes =        "Special Issue on Genetic Algorithms

                 Rajesh Jha and Frank Pettersson and George S.
                 Dulikravich and Henrik Saxen and Nirupam Chakraborti",
}

Genetic Programming entries for Rajesh Jha Frank Pettersson George S Dulikravich Henrik Saxen N Chakrabortic

Citations