Kindergarten in Espoo / Kindergarten in Espoo

Miettinen, Samuli; Jaaksi, Asmo; Kurkela, Teemu; Mäki-Jyllilä, Juha

doi:10.11129/9783955533113-008

Cited by 3 publications

(4 citation statements)

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“…Again, no experimental measurements are available for these example calculations, but two continuous cooling precipitation (CCP) curve validations for Nb(C,N) have been presented in Section 4.8. In addition, several equilibrium condition calculations on the austenite-carbonitride equilibria in low-alloyed steels have been validated with the corresponding measurements, in the study by Miettinen et al [36] Figure 12 shows the calculated amounts of NbC in steel A solidified at two rates (CRS ¼ 1 °C s À1 and CRS ¼ 10 °C s À1 ) going through two different post-treatments: heating at 0.5 °C s À1 and holding for 20 min at 1100 °C (red curves) and heating at 10 °C s À1 and holding for 2 min at 1200 °C (blue curves). In both cases, the final cooling rate is assumed to be 10 °C s À1 .…”

Section: Effect Of Post-treatment On Precipitationmentioning

confidence: 92%

Section: Calculations Using the Sol Modulementioning

confidence: 99%

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Simulation of the Solidification and Microstructural Evolution in Steel Casting Processes Using the InterDendritic Solidification Tool

Miettinen

Somani

Visuri

et al. 2022

steel research int.

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InterDendritic Solidification (IDS) is a thermodynamic-kinetic software combined with a microstructure tool developed to simulate the nonequilibrium solidification (non-EQS) of steels. Herein, its main calculation module, solidification (SOL), is introduced, and some essential results of that module, such as the formation of ferrite and austenite in different types of steels during their solidification, and the formation and dissolution of precipitates during subsequent cooling and heating processes, respectively, following solidification, are presented. The non-EQS is compared with equilibrium and poor-kinetics solidification to demonstrate the effect of kinetics on the results using finite solute diffusion and microstructure data. The poor-kinetics solidification is comparable with the modified Scheil simulation ignoring the solid-state diffusion of slowly moving metallic elements. A particular emphasis is made on demonstrating how to use a postprocessing treatment to control the residual ferrite amounts in stainless steels and the extent of precipitation in particular steel. In this context, the phenomena occurring behind the results are discussed. Finally, to validate the simulations of the SOL module, its calculations are compared with numerous solidification measurements, such as the liquidus and solidus temperatures of different steels and the residual ferrite amounts in stainless steels.

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Section: Effect Of Post-treatment On Precipitationmentioning

confidence: 92%

Section: Calculations Using the Sol Modulementioning

confidence: 99%

Simulation of the Solidification and Microstructural Evolution in Steel Casting Processes Using the InterDendritic Solidification Tool

Miettinen

Somani

Visuri

et al. 2022

steel research int.

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“…[28] To obtain the continuous cooling diagram (CCT), the TTT is transformed by the Scheil-Avrami, or additivity, rule. [28] At this stage, all thermophysical material data is calculated as a function of steel composition and cooling rate: thermal conductivity and density, [29] effective heat capacity, [8,30] and phase fractions of solid, liquid, ferrite, austenite, and pearlite versus temperature. Please note that within the current CCT calculation, the cooling rate is fixed at 0.1 °Cs À1 .…”

Section: Materials Data: Microsegregation High-temperature Phase Tran...mentioning

confidence: 99%

A Near‐Process 2D Heat‐Transfer Model for Continuous Slab Casting of Steel

Bernhard

Santos

Preuler

et al. 2022

steel research int.

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Continuous casting has become a highly developed technology for producing steel grades with the highest surface and internal quality. Adequate numerical simulation tools have accompanied this enormous progress in process development with practical relevance for continuous casting. Computation modeling so far addresses a wide variety of the phenomena involved such as heat transfer, fluid flow, thermomechanics, phase transformations, and all aspects of product quality, as recently reviewed by Thomas. [1] For process control, standard solidification models solve the heat-transfer equation in 2D or 3D under stationary or transient boundary conditions and provide information about shell growth and temperature distribution. [2] For online process models, the quality of the thermal boundary conditions and the thermophysical properties of the cast steel grade is decisive for the accuracy of the calculation results.The working group M 2 CC at Montanuniversitaet Leoben has been working on the experimental and numerical simulation of quality-relevant aspects of casting for decades. Meanwhile, the characterization of phase transformations in steels [3][4][5] and the assessment of thermodynamic databases [6] have become a further research focus. The numerical models, databases, measurement techniques, and laboratory experiments developed are linked in the noncommercial offline "casting development platform" m 2 CAST, schematically illustrated in Figure 1. [7] The main emphasis behind the solidification model m 2 CAST is the transfer of process information to lab experiments and vice versa. The general concept and individual steps can be summarized as follows:Thermodynamic data in the material module m 2 MAT is based on extensive internal differential scanning calorimetry (DSC) measurements and thermodynamic calculations, for example, by FactSage 8.0 software and its databases. [8] The calculated thermophysical properties of the steel grade of interest, for example, phase fractions, heat capacity, thermal conductivity, and density, are required to solve the heat conduction equation.In the caster configurator, the slab dimensions and geometry, that is, thickness, width, and casting gap, must be defined along with the mesh size.

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“…IDS is a thermodynamic-kinetic-empirical software package, which has been developed since 1984 to simulate solidification and cooling/heating related microstructural phenomena, such as phase transformations, solute microsegregation, inclusion and precipitate formation, precipitate dissolution, grain growth and austenite decomposition [19][20][21][22][23]. The package has since been validated with extensive data of solidification experiments [19,21,24]. Recently, the IDS database has been extended to enable improved simulations of new steel grades [25,26].…”

Section: Ids and Castmanager/tempsimu Toolsmentioning

confidence: 99%

New phenomenological quality criteria for continuous casting of steel based on solidification and microstructure tool IDS

Louhenkilpi

Miettinen

Visuri

et al. 2020

Ironmaking & Steelmaking

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Although the formation of defects in continuous casting has been studied for several decades, a deeper understanding and improved quality prediction concepts are needed to correlate the defects with the actual steel compositions, machine data and casting parameters. The aim of this work was to derive new quality criteria based on steel composition and cooling pattern for continuous casting and for the subsequent cooling and reheating processes. The criteria were devised based on the outputs of multiphysics simulation tools for online casting applications. The developed criteria were found to be good predictors of whether a steel grade combined with a given cooling pattern is prone to a specific defect. The criteria useful in providing a theoretical justification as to why certain defects form or would form, and can be used for devising practical solutions to avoid them. In practice, the final determination of whether a defect will form depends on the cumulative impact of various single quality criteria combined with the models/data describing the mechanical and thermal stresses forming. The criteria proposed are applicable as part of online quality prediction systems in casting machines. In this paper, new quality criteria were proposed for different kind of cracking-related and gas defects along with case examples. In further work, the criteria will be used to develop rule-based online control systems, in which cumulative effects of individual quality criteria are combined with data from mechanical and thermal stress calculations.

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Kindergarten in Espoo / Kindergarten in Espoo

Cited by 3 publications

References 0 publications

Simulation of the Solidification and Microstructural Evolution in Steel Casting Processes Using the InterDendritic Solidification Tool

Simulation of the Solidification and Microstructural Evolution in Steel Casting Processes Using the InterDendritic Solidification Tool

A Near‐Process 2D Heat‐Transfer Model for Continuous Slab Casting of Steel

New phenomenological quality criteria for continuous casting of steel based on solidification and microstructure tool IDS

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