- 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",
