Arrhenius constitutive equation and artificial neural network model of flow stress in hot deformation of offshore steel with high strength and toughness

ABSTRACT

In this study, the thermal deformation behaviour of a high strength offshore steel at different temperatures and rates was investigated through thermal compression experiments.An Arrhenius constitutive model and a back propagation artificial neural network (BP-ANN) were established to address more complex deformation characteristics. The performances of both models were was evaluated using standard statistical parameters such as the correlation coefficient (R) and average absolute relative error (AARE). The results showed that both models can accurately predict the rheological stresses generated during deformation.The BP-ANN outperforms the Arrhenius equation model with correlation coefficients of fit greater than 99.9% and less than 0.8% relative error. At a strain rate of 0.01 s⁻¹ and 10 s⁻¹, the accuracy of the ANN decreases slightly due to the fact that it exceeds the strain rate range of the training set, as compared to the Arrhenius constitutive equations as these are more accurately predicted.

KEYWORDS:

• High strength offshore steel
• compression experiment
• Arrhenius constitutive equation
• back-propagation artificial neural network
• flow stress

Acknowledgments

This research was financially supported by Science and Technology Research Projects of the CNPC (No.2021ZG14 and LHT-2023-02).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Science and Technology Research Projects of CNPC (No.2021ZG14 and LHT-2023-02).