Flow Stress Model for V-N Microalloyed Steel Under Multi-pass Deformation Conditions

doi:10.12068/j.issn.1005-3026.2025.20230261

Abstract

Abstract:

In order to establish the flow stress models of V-N microalloyed steel under multi-pass deformation conditions， multi-pass compression experiments were conducted on experimental steel using a DIL805 thermal expansion phase transformation tester， and the stress-strain curves were plotted. The flow stress of experimental steel was simulated with high accuracy by Hensel-Spittel model under single-pass deformation conditions. When the deformation temperature and strain rate remained constant， genetic algorithm was used to optimize the parameters of the Hensel-Spittel model under multi-pass deformation conditions. The support vector machine（SVM） algorithm was used to establish the corresponding relationships between the static recrystallization volume fraction before deformation， austenite grain size before deformation， dislocation density， deformation temperature， strain rate and the model parameters. The results show that the predicted flow stress under multi-pass deformation conditions is in good agreement with the measured values. The research results can provide strong support for accurately describing the flow stress of V-N microalloyed steel under multi-pass deformation conditions.

Key words: flow stress, genetic algorithm, static recrystallization, dislocation density, support vector machine algorithm

CLC Number:

TG 142.1

Xiao-guang ZHOU, Jin-fan ZHAO, Shan JIANG, Guang-ming CAO. Flow Stress Model for V-N Microalloyed Steel Under Multi-pass Deformation Conditions[J]. Journal of Northeastern University(Natural Science), 2025, 46(2): 35-41.

Figures/Tables 12

Fig. 1 Schematic diagram of the austenite quenching experiment

Table 1 Regression parameters of the flow stress model

a₀	a₁	a₂	a₃	a₄
8 119.545	-2.567	0.098	0.402	-1.171

Fig. 2 Comparison of flow stress predicted by the Hensel-Spittel model with measured values

Fig. 3 Schematic diagram for calculating static softening rate

Table 2 Parameters of t0.5 equation

A	n₁	n₂	n₃	Q/（J·mol^-1）
6.58×10^-34	-12.27	-1.058	3.720 97	423 057

Fig. 4 Comparison of static recrystallization volume fraction

Fig. 5 Austenite microstructure of experimental steel before deformation in each pass

Fig. 6 Comparison of predicted flow stress adopting genetic algorithm optimization and measured values

Fig. 7 Flow chart of predicting flow stress adopting SVM algorithm

Fig. 8 Comparison of predicted flow stress and measured values under double-pass deformation

Fig. 9 Comparison of predicted flow stress and measured values for three passes deformation

Fig. 10 Comparison of predicted flow stress and measured values for the 3rd deformation

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