Improving the prediction accuracy of constitutive model with ANN models

Author

Kong, L.X. ; Hodgson, P.D.

Author_Institution

Sch. of Eng. & Technol., Deakin Univ., Geelong, Vic., Australia

Volume

1

fYear

1999

fDate

36342

Firstpage

389

Abstract

The unified constitutive model developed by Estrin and Mecking (1984) has successfully been used in hot rolling to provide information for the control of strip thickness. It has presented a high accuracy in predicting the hot strength of austenitic steels. However, the materials can show quite different properties under different deformation conditions and the constitutive models are not able to be generalised to cover a wide range of compositions and deformation conditions, therefore, the potential of those model is limited. In this work, the robustness of the unified constitutive model is enhanced by incorporating an artificial neural network model to predict the flow strength of austenitic steels with carbon content ranging from 0.0037 to 0.79%

Keywords

austenitic steel; hot rolling; mechanical strength; mechanical variables control; plastic deformation; stress-strain relations; work hardening; ANN models; artificial neural network model; austenitic steels; constitutive model; constitutive models; deformation conditions; flow strength; hot rolling; hot strength; prediction accuracy improvement; strip thickness control; Accuracy; Artificial neural networks; Australia; Capacitive sensors; Deformable models; Predictive models; Steel; Stress; Temperature; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Processing and Manufacturing of Materials, 1999. IPMM '99. Proceedings of the Second International Conference on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-5489-3

Type

conf

DOI

10.1109/IPMM.1999.792511

Filename

792511