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Development of Constitutive Models for Extrapolative Prediction of Nb–Ti Micro Alloyed Steel
Author(s) -
Wu Siwei,
Zhou Xiaoguang,
Chen Qiyuan,
Ren Jiakuan,
Cao Guangming,
Liu Zhenyu
Publication year - 2017
Publication title -
steel research international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.603
H-Index - 49
eISSN - 1869-344X
pISSN - 1611-3683
DOI - 10.1002/srin.201700082
Subject(s) - materials science , flow stress , arrhenius equation , artificial neural network , constitutive equation , stress (linguistics) , deformation (meteorology) , strain rate , metallurgy , thermodynamics , composite material , computer science , artificial intelligence , activation energy , finite element method , physics , linguistics , chemistry , philosophy , organic chemistry
The experimental stress–strain data from hot compression tests at temperatures from 900 to 1050 °C and strain rates of 0.1, 1, and 10 s −1 are used to develop the constitutive models to predict flow stress of Nb–Ti micro alloyed steel. The Arrhenius‐type model considering compensation of strain, Bayesian regularization neural network model, and integrated model are investigated. The results show that although the Arrhenius‐type model considering compensation of strain can predict the correct variation trends under different deformation conditions, the accuracy is far from being satisfactory. On the other hand, the Bayesian regularization neural network model shows high accuracy in the training data range, but rather uncertainty for the data outside the training data range. By adding stress predicted by the Arrhenius‐type model considering compensation of strain and characteristic points (critical stress, peak stress, and steady–state stress) to neural network model's inputs, the integrated model can result in accurate prediction for hot deformation behavior of Nb–Ti micro alloyed steel, showing more promising potential for industrial application.