Open Access
Review
Table 1
The comparison and analysis of these prediction models of globularization.
| Model name | Empirical model | Neural network model | Processing map |
|---|---|---|---|
| Model description | The equations of Avrami and JMAK Form of explicit functions of macroscopic deformation parameters [17] |
Complex nonlinear processing power Related input variables and output variables in a near-black box approach [19] |
A power dissipation map represents the pattern in which the input power is dissipated by the material through microstructural changes rather than heat [61] |
| Model equation |
|
|
|
| Pros and cons | ■ Predict the grain size volume fraction □ Predict the morphology evolution □ Predict Meso-mechanical response ■ Predict Macro-mechanical response ■ Consider hot working parameters |
■ Predict the grain size volume fraction □ Predict the morphology evolution □ Predict Meso-mechanical response ■ Predict Macro-mechanical response ■ Consider hot working parameters |
□ Predict the grain size volume fraction □ Predict the morphology evolution □ Predict Meso-mechanical response ■ Predict Macro-mechanical response ■ Consider hot working parameters |
| Model name | Internal variable model | Phase field model | Microstructure-based FE model |
| Model description | Describe the microstructure evolution through the evolution of several of different internal state variables. Reflect the complex physical mechanism Inflect influence of processing history on microstructure [3] |
Multiphase field model driven by volume-diffusion and curvature effect Shape-instabilities induced by the difference in curvature Based on Rayleigh instabilities and termination migration [24] |
Digital material representation method The constitutive relation measured by the nanometer indentation The effects of microstructure features on strain localization bands [25] |
| Model equation |
|
|
|
| Pros and cons | ■ Predict the grain size volume fraction □ Predict the morphology evolution □ Predict Meso-mechanical response ■ Predict Macro-mechanical response ■ Consider hot working parameters |
□ Predict the grain size volume fraction ■ Predict the morphology evolution □ Predict Meso-mechanical response □ Predict Macro-mechanical response ■ Consider hot working parameters |
□ Predict the grain size volume fraction □ Predict the morphology evolution ■ Predict Meso-mechanical response □ Predict Macro-mechanical response ■ Consider hot working parameters |
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