A Dislocation Density‐Based Unified Constitutive Model of Multipass Deformation. Issue 2 (29th November 2019)

• Record Type: Journal Article
• Authors: Jia, Bin; Ma, Hongru; Peng, Yan
• Is Part Of: Steel research international. Volume 91:Issue 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-11-29
• Subjects: dislocation density model -- microstructural model couple -- multipass deformation -- parameters identification -- unified constitutive model; Steel -- Periodicals; Steel -- Metallurgy -- Periodicals; 669.142
• DOI: 10.1002/srin.201900372
• Languages: English
• ISSNs: 1611-3683

Abstract

Abstract : Quantitative relations between the softening mechanism and the constitutive model are determined based on the dislocation density, the influences of various deformation parameters, the dynamic softening mechanism, and static softening mechanism. A unified explicit constitutive model of multipass deformation is established by coupling the microstructural model of Q345B steel (the equivalent of ASTM A572Gr50). The flow stress curves under different deformation conditions are obtained from a Gleeble‐3500 thermosimulation machine during hot compression deformation, including single‐pass and double‐pass compression for Q345B. From the experimental results, a new method to identify parameters is developed to determine the material parameters for the unified constitutive model and to establish their analytic expressions. The results predicted from the proposed model are also presented for validation and compared with the experimental results. The proposed model can accurately describe the stress–strain curves during dynamic recovery on the high‐stress stage, during dynamic recrystallization, steady flow state, and next pass after the completion of static softening. In addition, the errors from the model are adequately analyzed to make further improvements. Abstract : A unified constitutive model of multi‐pass deformation is established with microstructural models coupled sufficiently. Meanwhile, a new method of parameter identification is provided in the less computationally expensive way. The proposed model can accurately describe the stress–strain curves during dynamic recovery on the stage of high stress, during dynamic recrystallization, steady flow state, and next pass after completing static softening finished.