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Advances in Manufacturing
Erschienen in: Advances in Manufacturing 2/2019

30.05.2019

A strategy to control microstructures of a Ni-based superalloy during hot forging based on particle swarm optimization algorithm

verfasst von: Dong-Dong Chen, Yong-Cheng Lin, Xiao-Min Chen

Erschienen in: Advances in Manufacturing | Ausgabe 2/2019

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Abstract

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.
Vorheriger Artikel Mechanism of brittle fracture in diamond turning of microlens array on polymethyl methacrylate
Nächster Artikel Structural, ferromagnetic, and optical properties of Fe and Al co-doped ZnO diluted magnetic semiconductor nanoparticles synthesized under high magnetic field

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Metadaten
Titel
A strategy to control microstructures of a Ni-based superalloy during hot forging based on particle swarm optimization algorithm
verfasst von
Dong-Dong Chen
Yong-Cheng Lin
Xiao-Min Chen
Publikationsdatum
30.05.2019
Verlag
Shanghai University
Erschienen in
Advances in Manufacturing / Ausgabe 2/2019
Print ISSN: 2095-3127
Elektronische ISSN: 2195-3597
DOI
https://doi.org/10.1007/s40436-019-00259-0

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