Microstructure–Property Relationship in Cold-Drawn Pearlitic Steel Wires

Guelton, N.; François, Marc

doi:10.1007/s11661-019-05613-2

Cited by 15 publications

(5 citation statements)

References 82 publications

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“…( 4)). Moreover, although friction stress 𝜏 𝑓 in low-alloyed ferrite is typically of the order of some tens of MPa (Peierls stress plus solid solution strengthening), the initial effective dislocation density in patented eutectoid steels can be very large (≈ 2.6 ⋅ 10 15 m −2 ) as observed by Guelton and François [58], which justifies the large values of (𝜏 𝑓 + 𝜏 0 ) in our calibration (the same authors also show that rate of dislocation storage in pearlitic ferrite at large strains is small, in agreement with our model results of its very weak strain hardening). The best fit value of 𝐴 was slightly higher than that typically reported in literature (1 ≤ 𝐴 ≤ 1∕(1 − 𝜈)).…”

Section: Model Calibrationmentioning

confidence: 97%

A Microstructure-Based Constitutive Model for Eutectoid Steels

Rodríguez-Páez,

Dorronsoro,

Martinez-Esnaola

et al. 2023

Preprint

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Section: Model Calibrationmentioning

confidence: 97%

A Microstructure-Based Constitutive Model for Eutectoid Steels

Rodríguez-Páez,

Dorronsoro,

Martinez-Esnaola

et al. 2023

Preprint

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“…These wires are manufactured by subjecting hot-rolled steel rods to multiple cold drawing processes, resulting in a rapid increase in strength with the increase in cold drawing strain. Research has shown that the strengthening mechanisms of pearlitic steel wire during the cold drawing process can be attributed to three aspects [3,4]: (1) The interlamellar spacing decreases with the increase of cold drawing strain, resulting in fine grain strengthening and the relationship between interlamellar spacing and strength can be described by the Hall-Petch equation; (2) Dislocations in the pearlite increase rapidly during cold drawing, leading to dislocation strengthening; (3) The dissolution of cementite occurs during cold drawing, and the dissolved carbon atoms diffuse into the ferrite, resulting in solid solution strengthening.…”

Section: Introductionmentioning

confidence: 99%

“…As known above, multiple changes in the internal structure of pearlite occur simultaneously during the cold drawing process, and the alteration in mechanical properties arises from the combined effect of these structural variations. Previous researchers have primarily relied on experimental methods to investigate the evolution of microstructure and performance of pearlite steel wires during cold drawing, but these methods are unable to separately examine the influence of cementite structure transformation on the mechanical properties of pearlite [1][2][3][4]. Molecular dynamics (MD), as a simulation research method, provides a unique opportunity to study material properties under desired conditions [8,9].…”

Section: Introductionmentioning

confidence: 99%

Molecular dynamics study on the effect of cementite structure on the mechanical properties of pearlite

Zhang,

Chen,

Sun

2024

Phys. Scr.

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The effect of cementite structure on the mechanical properties of pearlite were investigated using molecular dynamics simulations. Three types of cementite structures were considered: single crystal, nanocrystalline, and a mixture of nanocrystalline and amorphous structures. The study found that regardless of the cementite structure, the ferrite phase in the pearlite exhibited plastic deformation first due to the activation of its internal {112} <111> slip system during tensile loading, resulting in macroscopic yielding of the pearlite. However, the difficulty of plastic deformation in the ferrite phase varied with different cementite structures. Compared to the other two types of cementite structures, the ferrite phase in the pearlite containing single crystal cementite exhibited the most difficult plastic deformation, leading to the highest peak stress. As the strain increased, the plastic deformation in the ferrite transferred through the ferrite-cementite interface to the cementite. After the plastic deformation transferred to the cementite, the different cementite structures exhibited distinct mechanical responses: the single crystal cementite structure experienced cleavage fracture, the nanocrystalline cementite structure underwent plastic deformation induced by grain boundary slip, while the nanocrystalline-amorphous mixture cementite structure showed shear deformation in the amorphous cementite and plastic deformation induced by grain boundary slip in the nanocrystalline cementite. Due to the influence of cementite mechanical response mechanism, the flow stress of the pearlite was minimal when the cementite in the pearlite were in single crystal structure. The study also revealed that when the cementite was in nanocrystalline structure, the flow stress of pearlite decreases with the grain size decreasing from 4.25 nm to 2.68 nm. When the cementite is a mixture of nanocrystalline and amorphous structure, the flow stress of pearlite decreases as the thickness of amorphous layer increases from 1 nm to 2 nm.

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“…[ 1–3 ] The carbon steels with a pearlitic microstructure have been receiving intensive attention due to its incomparable superiority in strength properties. [ 4–8 ] Generally known that the pearlite is composed of colonies of similarly arranged lamellas of ferrite and cementite, [ 9 ] pearlite microstructures can be regarded as a model composite from the view of interactions between hard cementite and soft ferrite at the microscopic scale. In view of the modern ideas, the phase constitution serves as an indicator of its material properties.…”

Section: Introductionmentioning

confidence: 99%

Phase Prediction of High Carbon Pearlitic Steel: An Improved Model Combining Mind Evolutionary Algorithm and Neural Networks

et al. 2021

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Accurate phase constitution prediction is crucial for guiding the new steel design with desirable properties. This article uses three machine learning (ML) algorithms, backpropagation neural network (BPNN), radial basis function neural network (RBFNN), and fuzzy neural network (FNN), to compare the accuracy of predictive model. Toward the collected data, statistical measure of correlation is taken in the present work by determining Pearson correlation coefficient (PCC). The results show that the testing accuracy using BPNN is higher than the corresponding testing accuracy using RBFNN and FNN. With these understandings, the well‐known optimization algorithms, namely, mind evolutionary algorithm (MEA), are implemented to find optimal hyperparameters set that is able to induce a higher predictive capability. The resulting MEA‐BPNN further enhances the prediction results in the present dataset and efficiently explores the relationship between alloying elements and phase constitution. Finally, the reliability and practicability of the model are verified by experimental measurement on the ferrite content for prepared steels, and their mechanical properties are tested for engineering applications. As such, the work proposes a useful MEA‐BPNN model for predicting the phases of high carbon pearlite steel and provides an alternative route of designing new steels in a given system.

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Microstructure–Property Relationship in Cold-Drawn Pearlitic Steel Wires

Cited by 15 publications

References 82 publications

A Microstructure-Based Constitutive Model for Eutectoid Steels

A Microstructure-Based Constitutive Model for Eutectoid Steels

Molecular dynamics study on the effect of cementite structure on the mechanical properties of pearlite

Phase Prediction of High Carbon Pearlitic Steel: An Improved Model Combining Mind Evolutionary Algorithm and Neural Networks

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