A comparison of bending-fatigue properties of surface-induction-hardened SAE 1045 bar steels with and without vanadium and the influence of comparable low-temperature induction-tempering and furnace-tempering treatments

Thompson, S. W.; Parthasarathi, Venkkateesh; Findley, Kip O.

doi:10.1016/j.msea.2021.140812

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…The hardness was measured using HRS‐150 Rockwell hardness tester at room temperature and at least 10 points were measured on each specimen. The Rockwell hardness was converted to Vickers hardness to substitute hardness values into different models, as defined by Equation (1): [ 20,21 ]

\text{HV=} \frac{223 \times \text{HRC} + 14500}{100 - \text{HRC}}

…”

Section: Methodsmentioning

confidence: 99%

Softening Behavior and Hardness Prediction of H13 Hot‐Work Die Steel during Isothermal Tempering

Feng,

Wang,

et al. 2023

steel research int.

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In this study, the microstructural evolution of H13 hot‐work die steel during isothermal tempering at 500∽650 °C was evaluated, and kinetic equations and a neural network were constructed to describe the softening behavior. The results showed that the softening behavior appeared as a loss of hardness with increasing isothermal tempering temperature and time. The softening mechanism was ascribed to the decomposition of martensitic laths, annihilation and rearrangement of dislocations, formation of sub‐grains and precipitation and coarsening of secondary carbides. The evolution of secondary carbides included the coarsening of V‐rich MC nanoprecipitates, and the precipitation and coarsening of secondary phases (Cr‐rich M23C6 and Mo‐rich M6C carbides) during long‐term isothermal tempering. Regarding the applicability of the model in precipitation evolution and hardness prediction, the Johnson‐Mehl‐Avrami (JMA) model was considered to be more suitable than the Hollomon‐Jaffe (H‐J), Lifshitz‐Slyozov‐Wagner (LSW) and back‐propagation neural network (BPNN) models.This article is protected by copyright. All rights reserved.

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\text{HV=} \frac{223 \times \text{HRC} + 14500}{100 - \text{HRC}}

…”

Section: Methodsmentioning

confidence: 99%

Softening Behavior and Hardness Prediction of H13 Hot‐Work Die Steel during Isothermal Tempering

Feng,

Wang,

et al. 2023

steel research int.

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“…given in Figure 1 were determined in rotation bending tests and the deviation observed for materials with a higher hardness is partially caused by residual stresses at the surface [11]. As this loading condition results in a stress gradient from the surface to the inner material, an increased influence of residual stresses in relation to uniaxial fatigue testing occurs.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Estimate the Fatigue Limit of Steels Based on Conventional and Cyclic Indentation Testing

Görzen

Ostermayer

Lehner

et al. 2022

Metals

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For a reliable design of structural components, valid information about the fatigue strength of the material used is a prerequisite. As the determination of the fatigue properties, and especially the fatigue limit σw, requires a high experimental effort, efficient approaches to estimate the fatigue strength are of great interest. Available estimation approaches using monotonic properties, e.g., Vickers hardness (HV), and in some cases the cyclic yield strength, only allow a rough estimation of σw. The approaches solely based on monotonic properties lead to substantial deviations of the estimated σw in relation to the experimentally determined fatigue limit as they do not consider the cyclic deformation behavior. In this work, an estimation approach was developed, which is based on a correlation analysis of the fatigue limit σw, HV, and the cyclic hardening potential obtained in instrumented cyclic indentation tests (CIT). For this, eleven conditions from five different low-alloy steels were investigated. The CIT enable an efficient and quantitative determination of the cyclic hardening potential, i.e., the cyclic hardening exponentCHT eII, and thus, the consideration of the cyclic deformation behavior in an estimation approach. In this work, a strong correlation of σw with the product of HV and |eII| was observed. In relation to an existing estimation approach based solely on HV, considering the combination of HV and |eII| enables the estimation of σw with an enormously increased precision.

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Influence of induction hardening on the damage tolerance of EA4T railway axles

Gao

Han

Zhu

et al. 2023

Engineering Failure Analysis

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A comparison of bending-fatigue properties of surface-induction-hardened SAE 1045 bar steels with and without vanadium and the influence of comparable low-temperature induction-tempering and furnace-tempering treatments

Cited by 3 publications

References 28 publications

Softening Behavior and Hardness Prediction of H13 Hot‐Work Die Steel during Isothermal Tempering

Softening Behavior and Hardness Prediction of H13 Hot‐Work Die Steel during Isothermal Tempering

A New Approach to Estimate the Fatigue Limit of Steels Based on Conventional and Cyclic Indentation Testing

Influence of induction hardening on the damage tolerance of EA4T railway axles

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