Calculation of Crystallographic Texture of BCC Steels During Cold Rolling

Das, Avimanyu

doi:10.1007/s11665-017-2695-6

Cited by 11 publications

(5 citation statements)

References 81 publications

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“…It is demonstrated by Joo et al [9] that the texture plays a major role in influencing the anisotropy of the Charpy energy of pipeline steel. Improvement in different physical and mechanical properties of these polycrystalline grain structures and hence increasing their performances by modifying and controlling their microstructure/texture has been of considerable interest in recent years [9][10][11][12]. Reactor pressure vessels are used in thick sections, which, when quenched, experience varying cooling rates along the thickness sections.…”

Section: Introductionmentioning

confidence: 99%

Effect of Cooling Rate on the Microstructure of a Pressure Vessel Steel

Das

Kapoor

2019

Metallogr. Microstruct. Anal.

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The present article thoroughly explores the effect of cooling rate on various microstructural features of a reactor pressure vessel steel (20MnMoNi55). For this, dilatometric experiments were performed at different cooling rates. Optical microscopy and electron backscatter diffraction were used to measure different microstructural parameters. Continuous cooling from 0.15 to 0.3 °C/s revealed ferrite and bainite; only bainite is seen from 1 to 2 °C/s, both bainite and martensite were found in the regime 5 to 15 °C/s, and complete martensitic transformation was observed at cooling rate 20 °C/s and above. With increase in cooling rate, the fraction of low angle boundary decreased and that of high angle boundary increased. The nature of grain size distribution was found to be cooling rate sensitive.

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Section: Introductionmentioning

confidence: 99%

Effect of Cooling Rate on the Microstructure of a Pressure Vessel Steel

Das

Kapoor

2019

Metallogr. Microstruct. Anal.

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“…Figure 16 shows the ODF sections for ϕ 2 = 45 • as predicted by the β-model using the {110} + {112} slip systems and the pencil Figure 13. Description of α-fiber and γ-fiber, after [55]. glide models.…”

Section: Rollingmentioning

confidence: 99%

Efficient simulation of single and poly-crystal plasticity based on the pencil glide mechanism

Le¹,

Ammar²,

Forest³

2021

Comptes Rendus. Mécanique

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“…The ranges inside which the parameters of the texture model vary are defined such that typical bcc rolling textures found in literature can be represented, cf. [53,54,55,41,56,48,57]. The parameter ranges are listed in Tab.…”

Section: Texture-property Data Setmentioning

confidence: 99%

A multi-task learning-based optimization approach for finding diverse sets of material microstructures with desired properties and its application to texture optimization

Iraki¹,

Morand²,

Dornheim³

et al. 2021

Preprint

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The optimization along the chain processing-structure-propertiesperformance is one of the core objectives in data-driven materials science. In this sense, processes are supposed to manufacture workpieces with targeted material microstructures. These microstructures are defined by the material properties of interest and identifying them is a question of materials design. In the present paper, we addresse this issue and introduce a generic multi-task learning-based optimization approach. The approach enables the identification of sets of highly diverse microstructures for given desired properties and corresponding tolerances. Basically, the approach consists of an optimization algorithm that interacts with a machine learning model that combines multi-task learning with siamese neural networks. The resulting model (1) relates microstructures and properties, (2) estimates the likelihood of a microstructure of being producible, and (3) performs a distance preserving microstructure feature extraction in order to generate a lower dimensional latent feature space to enable efficient optimization. The proposed approach is applied on a crystallographic texture optimization problem for rolled steel sheets given desired properties.

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Calculation of Crystallographic Texture of BCC Steels During Cold Rolling

Cited by 11 publications

References 81 publications

Effect of Cooling Rate on the Microstructure of a Pressure Vessel Steel

Effect of Cooling Rate on the Microstructure of a Pressure Vessel Steel

Efficient simulation of single and poly-crystal plasticity based on the pencil glide mechanism

A multi-task learning-based optimization approach for finding diverse sets of material microstructures with desired properties and its application to texture optimization

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