In this study, a strategy based on the particle swarm optimization (PSO) algorithm is developed to control the microstructures of a Ni-based superalloy during hot forging. This strategy is composed of three parts, namely, material models, optimality criterions, and a PSO algorithm. The material models are utilized to predict microstructure information, such as recrystallization volume fraction and average grain size. The optimality criterion can be determined by the designed target microstructures and random errors. The developed strategy is resolved using the PSO algorithm, which is an intelligent optimal algorithm. This algorithm does not need a derivable objective function, which renders it suitable for dealing with the complex hot forging process of alloy components. The optimal processing parameters (deformation temperature and strain rate) are obtained by the developed strategy and validated by the hot forging experiments. Uniform and fine target microstructures can be obtained using the optimized processing parameters, which indicates that the developed strategy is effective for controlling the microstructural evolution during the hot forging of the studied superalloy.
The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-019-00259-0
Dong-Dong Chen
,
Yong-Cheng Lin
,
Xiao-Min Chen
. A strategy to control microstructures of a Ni-based superalloy during hot forging based on particle swarm optimization algorithm[J]. Advances in Manufacturing, 2019
, 7(2)
: 238
-247
.
DOI: 10.1007/s40436-019-00259-0
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