Role of microalloying elements in the microstructure of hot rolled steels

Gómez, Manuel; Medina, Sebastián F.

doi:10.3139/146.110585

Cited by 18 publications

(12 citation statements)

References 62 publications

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“…Alloying elements of the steel have an important effect on static recrystallization because recrystallization and grain growth activation energy are changed with respect to chemical composition [38,39]. An analytical model has to cover different steel categories and their chemical composition to reduce the prediction error.…”

Section: Models and Methodologymentioning

confidence: 99%

Prediction of mechanical properties of cold rolled and continuous annealed steel grades via analytical model integrated neural networks

Orta

Kayabasi

Şenol³

2019

Ironmaking & Steelmaking

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Mechanical property prediction is considerably demanded by steel manufacturers because it helps to avoid quality problems and increase productivity. However, prediction of yield strength, tensile strength and elongation of steel after annealing is a hard task because the relation between mechanical properties and process parameters like cold rolling reduction rate, annealing time, annealing temperature and alloying elements of steel is highly nonlinear. Moreover, analytical models mostly depend on experimental constants which are hard to obtain for industrial processes due to the wide range of the process parameters. Using neural network models is more practical, but it may lead to unphysical results. To increase accuracy and avoid unphysical results, analytical annealing models are redeveloped and integrated into the neural network models. Moreover, more than 50,000 tensile test data belonging to the production of past 3 years are used during model construction. Additionally, models are developed for different steel categories based on historical data, and these are aluminum killed, bake hardening, dual phase, high strength low alloy, interstitial free, and rephosphorized steels. According to the results, models have less than 1.7% error for tensile strength, 3.3% error for yield strength and 4.7% error for elongation.

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Section: Models and Methodologymentioning

confidence: 99%

Prediction of mechanical properties of cold rolled and continuous annealed steel grades via analytical model integrated neural networks

Orta

Kayabasi

Şenol³

2019

Ironmaking & Steelmaking

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“…Mn diffuses extremely slowly in austenite with a diffusion constant of m 2 s −1 at 636 C [68]. This is compared to Mn in ferrite with a diffusion constant of m 2 s −1 also at 636 C [68] or another substitutional element Al which has a diffusion constant on the order of m 2 s −1 at 600 C in austenite [69], both of which are nearly two orders of magnitude faster than Mn. Mn partitioning is therefore a common challenge in multi-phase 3Gen AHSS [70].…”

Section: Metallurgy Of Medium Mn Steelsmentioning

confidence: 99%

A review of the processing, microstructure and property relationships in medium Mn steels

Kwok

Dye

2023

International Materials Reviews

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Medium Mn steels are an emerging class of 3rd generation advanced high-strength steels. These steels have received significant attention due to their high strengths, large ductilities and also lower cost compared to their predecessor high Mn Twinning Induced Plasticity (TWIP) steels. Additionally, medium Mn steels have been found to exhibit TWIP and/or Transformation Induced Plasticity (TRIP) effects which can be harnessed to give a high strain hardening rate. Many thermomechanical processing concepts in the literature have been developed, producing multiple microstructure types with differentmechanical properties. The present review therefore aims to summarise the current knowledge of medium Mn steel alloy design especially on the processing, microstructure and property relationships in medium Mn steels. It complements the review of Sun et al. [Physical metallurgy of medium-Mn advanced high-strength steels, Int Mater Rev. 2023.], written independently and in parallel, which focusses more on the phase interfaces and thermodynamics.

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“…Apart from this, ample amount of studies can be found in the literature regarding the hot deformation behavior of microalloyed steels [13][14][15][16][17][18][19][20][21][22]. Although the precipitating phases are different in nature in Ni-base superalloys, these studies are particularly interesting from the perspective of current study because both 718Plus and microalloyed steels (especially with Nb) show similar precipitation -hot deformation behavior.…”

Section: Introductionmentioning

confidence: 97%

High-Temperature Deformation Behavior of 718Plus: Consideration of γ′ Effects

Özturk

Cabrera

Calvo

et al. 2020

Matls. Perf. Charact.

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The hot deformation behavior of 718Plus is modelled through a physically based hybrid dislocation density model which includes the effects of precipitating particles. It is well known that the service performance and hot-flow characteristics of this alloy are strongly dependent on the microstructure, particularly the grain size and second phase particles. Thus, comprehension and modelling of the hot flow behavior is an important task. In precipitation hardening alloys (superalloys, microalloyed steels, etc.) it is particularly more challenging to model the microstructural evolution, in the processing windows, where material softening and precipitation processes take place, concurrently. In this work, the initial stages of the deformation is studied.

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Role of microalloying elements in the microstructure of hot rolled steels

Cited by 18 publications

References 62 publications

Prediction of mechanical properties of cold rolled and continuous annealed steel grades via analytical model integrated neural networks

Prediction of mechanical properties of cold rolled and continuous annealed steel grades via analytical model integrated neural networks

A review of the processing, microstructure and property relationships in medium Mn steels

High-Temperature Deformation Behavior of 718Plus: Consideration of γ′ Effects

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