Simulation of deformation and static recrystallization in the stress relaxation test

Seppälä, Oskari; Pohjonen, Aarne; Ilmola, Joonas; Jokiranta, A.; Kaijalainen, Antti; Somani, Mahesh C.; Larkiola, Jari

doi:10.1088/1742-6596/1270/1/012027

Cited by 4 publications

(7 citation statements)

References 5 publications

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“…The recrystallization model was chosen to be a combination of the well-established 𝜕𝜕 50 -model and KJMA model, which have been studied and used extensively in previous studies, such as [8,9] . The models are presented here concisely, and the full details can be found from [10,11].…”

Section: Static Recrystallization Modelmentioning

confidence: 99%

Phenomenon-Based Model for Virtual Hot Strip Rolling

Ilmola,

Paananen,

Seppälä

et al. 2023

MSF

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Every year digitalization is taking a bigger role in the steel industry. Models for predicting metallurgical phenomena, roll forces and microstructure have been commonly used in development of novel steel grades. These individual models may predict certain phenomena thoroughly, but input values are usually based on an assumption or on a “good guess”. To produce reliable boundary conditions for these models of individual phenomena, a virtual rolling model is developed. This model computes the whole process of the hot strip mill from roughing to accelerated water cooling on a run-out table. Strip location and temperature evolution is calculated continuously. Thermal and thermo-mechanical (rolling stands) boundary conditions are according to process layout. Input data for the model is automatically read from raw process data. Rolling parameters are calculated using a coupled ARCPRESS model, which is developed by authors, and calculates normal and frictional shear stress distributions in the roll gap to predict roll forces and displacements of the work roll surface. Recrystallization is considered when calculating the flow stress of the rolled strip. Phase fractions during water cooling are calculated as well. The virtual rolling model minimizes the need for parameter speculation as all parameters are calculated throughout the process. All the input values are read from actual process data and the metallurgical and mechanical state of the strip are computed throughout the whole process. As required by the state-of-art virtual rolling model, this model is based on generally accepted theories and experimentally studied metallurgical and physical phenomena along with the thermo-mechanical response of the actual rolling process.

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Section: Static Recrystallization Modelmentioning

confidence: 99%

Phenomenon-Based Model for Virtual Hot Strip Rolling

Ilmola,

Paananen,

Seppälä

et al. 2023

MSF

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“…Recrystallization An effort has been made for construction and application of recrystallization models. The results of these studies are described in [20][21]. A physically based static recrystallization (SRX) model was implemented and presented in [21].…”

Section: Developed Modelsmentioning

confidence: 99%

Numerical Modelling of Thermo-Mechanical Processes in Steels - An Overview and Recent Progress

Pohjonen

Ilmola

Seppälä

et al. 2023

MSF

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Numerical modelling tools provide valuable means to quantitatively control thermomechanical processing. Several modelling tools have been applied and developed at University of Oulu during previous years, such as finite element models for hot rolling, recrystallization models, heat transfer and conduction model, coupled with phase transformation, as well as cellular automata and phase field models for simulating phase transformation during cooling. This article describes the overall development and recent progress of the developed numerical modeling tools.

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“…Assuming that the function 𝑔(𝑥) is sufficiently smooth, so that n:th order derivative exists for every 𝑥 , it can be expressed as n:th order Taylor polynomial 𝑔(𝑥) = Σ 0 𝑛 (𝑎 𝑛 𝑥 𝑛 ). Solving equation (1) for 𝑓(𝑥) and inserting it to equation (2) yields then equation (3).…”

Section: Polynomial Fittingmentioning

confidence: 99%

“…Many different models have been developed to simulate microstructural evolution. In [3], a finite element method (FEM) model for the thermomechanical simulator Gleeble 3800 was developed and a recrystallization model was implemented to simulate recrystallization in different parts around the test piece cross-section. In addition, various mean field models simulating the average grain size after recrystallization were investigated.…”

Section: Introductionmentioning

confidence: 99%

Analysis of grain size distribution evolution of steel during recrystallization and grain growth

Seppälä

Pohjonen

Ali

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Controlling the hot rolling process requires a deep understanding of the underlying metallurgical phenomena. Quantitative methods are of paramount importance for achieving the capability of controlling microstructural evolution. Since the final mechanical properties of steel result from microstructural evolution in the whole process, analysis of the microstructure provides an important input for numerical simulations that can be used for tailoring the mechanical properties of steel. The evolution of grain size distribution of a low-carbon CrNiMnB ultrahigh-steel in austenitic state is studied in hot forming and annealing using experimental data obtained with the Gleeble 3800 thermo-mechanical simulator. A general method is described that can be utilized to systematically compare the grain size distributions obtained from the experimental studies. The experimental data has been obtained from laser scanning confocal microscopy images using the mean linear intercept method. A custom-made semi-automatic software has been utilized to process the data rapidly and reliably.

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Simulation of deformation and static recrystallization in the stress relaxation test

Cited by 4 publications

References 5 publications

Phenomenon-Based Model for Virtual Hot Strip Rolling

Phenomenon-Based Model for Virtual Hot Strip Rolling

Numerical Modelling of Thermo-Mechanical Processes in Steels - An Overview and Recent Progress

Analysis of grain size distribution evolution of steel during recrystallization and grain growth

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