Analysis of preliminary local hardening close to the ferrite–martensite interface in dual-phase steel by a combination of finite element simulation and nanoindentation test

Matsuno, Takashi; Ando, Reon; Yamashita, Norio; Yokota, Hideo; Goto, Kenta; Watanabe, Ikumu

doi:10.1016/j.ijmecsci.2020.105663

Cited by 19 publications

(9 citation statements)

References 34 publications

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“…We found that the FE simulation of nanoindentation without the preliminary hardening site produced consistent results with the actual nano-hardness distribution around the ferrite-martensite interface. 17) Put differently, the preliminary hardening site was found to be absent in our previous study. However, we should consider that the nano-hardness involved a large degree of scattering.…”

Section: Analysis Of Nano-hardness Distribution Near the Ferritemartensite Interface In A Dual Phase Steel With Factorization Of Its Scatcontrasting

confidence: 55%

“…From our previous report, the nano-hardness of the ferrite exponentially decreases with an increase in the distance. 17) As shown in Figs. 5(α-1), 5(β-1), and 5(γ-1), nanoindentation was successfully performed very close to the ferrite-martensite interface.…”

Section: Sem Observationsmentioning

confidence: 79%

“…The nano-hardness at indent I3 deviated from the exponential decrease rule with an increase in the distance. 17) The nano-hardness at indent I3 presented almost linear-interpolated values at distances of 0 μm (at indent I10) and 0.4 μm (at indent I2) from the interface. The high KAM values may have caused this higher side deviation of the nano-hardness measurement.…”

Section: Discussionmentioning

confidence: 90%

“…Therefore, the nano-hardness measurements are converged at a distance exceeding the indent size; such a nano-hardness behavior is consistent with the results obtained from FE simulations. 17) If the preliminary hardening site was uniformly surrounding the ferrite-martensite interfaces, this convergence distance would never correspond with the indent size. However, we should note that the aforementioned nano-hardness convergence is the average trend in the measurements; the scattered nano-hardness measurements were not negligible.…”

Section: Discussionmentioning

confidence: 99%

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Analysis of Nano-hardness Distribution Near the Ferrite-martensite Interface in a Dual Phase Steel with Factorization of Its Scattering Behavior

et al. 2021

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Section: Analysis Of Nano-hardness Distribution Near the Ferritemartensite Interface In A Dual Phase Steel With Factorization Of Its Scatcontrasting

confidence: 55%

Section: Sem Observationsmentioning

confidence: 79%

Section: Discussionmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of Nano-hardness Distribution Near the Ferrite-martensite Interface in a Dual Phase Steel with Factorization of Its Scattering Behavior

et al. 2021

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“…In this context, the hardness test, in which the resistance to the imposed plastic deformation is measured on the surface, has been utilized as an efficient approach to estimate the mechanical properties in the industry. It can be used also to evaluate small-scale mechanical properties by controlling a small load; that is, the mechanical properties of a heterogeneous microstructure can be evaluated [1][2][3]. Using this technology, highthroughput evaluation of microscopic mechanical properties has been conducted to characterize the microscopic heterogeneity [4,5] and obtain massive material data [6].…”

Section: Introductionmentioning

confidence: 99%

Data-driven estimation of plastic properties of alloys using neighboring indentation test

Chen

Watanabe

Liu

et al. 2021

Science and Technology of Advanced Materials: Methods

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A data-driven estimation method for the plastic properties of alloys was proposed using indentations at neighboring positions. An instrumented indentation test is an efficient approach to measure mechanical properties such as equivalent elastic modulus and hardness. In the mechanical test, subsequent experiments are generally performed apart from existing indentations to avoid the interaction effect caused by the dependency on the deformation history of metal plasticity. In this study, the interaction effect was utilized to estimate the plastic properties, based on the difference of load-depth curves between the first and second indentations at neighboring positions. Using finite element simulations of the neighboring indentation tests, the effective experimental conditions were examined, and response surfaces of the loading curvatures were characterized to determine two material constants of a simple constitutive model of plasticity. Finally, the proposed approach was validated for application to aluminum alloys and stainless steel. It can be also applied to various alloys characterized by different elastic moduli. KEYWORDS elastic-plastic material; finite element method; mechanical testing; response surface; instrumented indentation test; material database; ARTICLE CLASSIFICATION: Data analysis (AI, machine learning, data-driven analysis, descriptor development, structure search/identification)

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Achieving Low Yield Ratio in High‐Strength Steel by Tuning Multiple Microstructures

Shi

Wang

Gao

et al. 2021

steel research int.

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Herein, martensite–ferrite dual‐phase high‐strength low‐alloy (HSLA) steels with a low yield ratio (YR), which is achieved through different heat‐treatment procedures, are evaluated in terms of multiple microstructures, e.g., grain size, soft‐phase proportion, and morphology. The extended austenitizing time exhibits a low YR by sacrificing both the yield and tensile strengths synchronously. By subtly regulating the tempering temperatures, the YR can be optimized from 0.93 to 0.89 after tempering at an applicable temperature. Moreover, by introducing the equivalent shape factor (ESF), the morphology effects of ferrite on the YR are investigated based on the microstructural observation and Abaqus finite element analysis. The YR is inversely proportional to the ESF of the microstructure, which can be used to guide the design of microstructural morphology. The correlation between the yield strength (YS), YR, and work hardening exponent is achieved using the Swift equation. The lower YR generates greater work hardening and reduced YS. By tuning multiple microstructures, high YS and relatively low YR are achieved via the secondary‐quenching intercritical heat treatment, which is beneficial for designing low‐yield‐ratio HSLA steels in industrial applications.

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Analysis of preliminary local hardening close to the ferrite–martensite interface in dual-phase steel by a combination of finite element simulation and nanoindentation test

Cited by 19 publications

References 34 publications

Analysis of Nano-hardness Distribution Near the Ferrite-martensite Interface in a Dual Phase Steel with Factorization of Its Scattering Behavior

Analysis of Nano-hardness Distribution Near the Ferrite-martensite Interface in a Dual Phase Steel with Factorization of Its Scattering Behavior

Data-driven estimation of plastic properties of alloys using neighboring indentation test

Achieving Low Yield Ratio in High‐Strength Steel by Tuning Multiple Microstructures

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