Thermomechanical processing design of nanoprecipitate strengthened alloys employing genetic algorithms

被引:0
|
作者
Rivera-Diaz-del-Castillo, Pedro E. J. [1 ]
de Jong, Maarten [2 ]
Sluiter, Marcel H. F. [2 ]
机构
[1] Univ Cambridge, Pembroke St, Cambridge CB2 3QZ, England
[2] Delft Univ Technol, Delft, Netherlands
来源
TMS2011 SUPPLEMENTAL PROCEEDINGS, VOL 2: MATERIALS FABRICATION, PROPERTIES, CHARACTERIZATION, AND MODELING | 2011年
关键词
alloy design; genetic modelling; ab initio; thermodynamics; optimisation; STAINLESS-STEELS; COMPUTATIONAL DESIGN; NEURAL-NETWORKS; THERMODYNAMICS; EMBRITTLEMENT; OPTIMIZATION; TEMPERATURE; PARAMETERS; TOUGHNESS; HYDROGEN;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A modelling strategy for designing nanoprecipitation strengthened alloys is presented here. This work summarises the application of a new thermokinetics approach wherein multiple design criteria are enforced: corrosion resistance and high strength combined with affordable thermomechanical processing schedules. The methodology presented here iteratively performs thermodynamic and kinetic calculations, these are aimed at determining the best precipitate nanostructures following multiple design objectives. A genetic algorithm is employed to more rapidly finding optimal alloy compositions and processing parameters consistent with the design objectives. It was possible to computationally design new alloys strengthened by Ni-based nanoprecipitates and carbides with yield strengths exceeding 1.6 GPa and good corrosion resistance. A major limitation in the methodology is the determination of optimum processing times, which require the computation of formation energies of non-equilibrium precipitates employing other techniques. A method to circumvent such limitation is introduced.
引用
收藏
页码:477 / 484
页数:8
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