Predicting properties and minimizing residual stress in quenched steel parts

Bates, Charles E.

doi:10.1007/bf02833162

Cited by 33 publications

(11 citation statements)

References 7 publications

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“…It has also been applied to steels [13][14] and aluminium casting alloys [15][16], and is now recognised as an important technique for modelling property losses during continuous cooling [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Improvements in quench factor modelling

Rometsch

Starink

Gregson

2003

Materials Science and Engineering: A

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In this contribution, the validity of a number of key quench factor analysis (QFA) assumptions is discussed. It is shown that the incorporation of a square-root dependency of yield strength on precipitate volume fraction provides a sounder physical basis for quench factor modelling. Peakaged strength/hardness prediction accuracies are not affected, but C-curve positions are. It is also demonstrated that transformation kinetics are described more correctly by a modified StarinkZahra equation than by a Johnson-Mehl-Avrami-Kolmogorov type equation, yielding better prediction accuracies when a physically realistic Avrami exponent of 1.5 or greater is used.Finally, a regular solution model is introduced to quantify the influence of the solute solubility temperature dependency on the minimum strength. These improvements are all implemented within the framework of classical QFA.

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Section: Introductionmentioning

confidence: 99%

Improvements in quench factor modelling

Rometsch

Starink

Gregson

2003

Materials Science and Engineering: A

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“…This implied that the remainder of the austenite transformed to produce a microstructure composed of 99.5 pct martensite and 0.5 pct nonmartensite. [16] In case of 80.0 pct, it meant that the microstructure after quenching consisted of 80.0 pct martensite and 20.0 pct non-martensite such as ferrite, pearlite, or bainite, which reduce the hardness or strength of the part. Therefore, the TTP diagram indicates that a low cooling rate during quenching increases the critical time, which is favorable to reduce the mechanical properties with the creation of a new phase such as ferrite or pearlite.…”

Section: Materials Constants and Ttp Diagram Of Boron Steelmentioning

confidence: 99%

“…[16] The sum of the incremental quench factor values over the transformation range between the M s and A c3 temperatures is the quench factor Q. The quench factor can be easily calculated from the cooling curve (or time-temperature curve).…”

Section: Quench Factor Analysis (Qfa)mentioning

confidence: 99%

Novel Method for Predicting Hardness Distribution of Hot-Stamped Part Using FE-Simulation Coupled with Quench Factor Analysis

Kim

2015

Metall Mater Trans B

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In the hot stamping of boron-alloyed steel, the mechanical properties of hot-stamped parts have been predicted by many empirical models based on Kirkaldy's equation. Although these models are skillfully developed, there is still a need to precisely and easily predict the mechanical properties in the hot stamping process. Therefore, this study aims to suggest a novel method for the accurate prediction of the hardness distribution of hot-stamped parts by the use of finite element (FE)-simulations coupled with quench factor analysis (QFA). First, dilatometry of boron steel was performed at various cooling rates from 0.5 to 70 K/s using a dilatometer with a forced-air cooling system. The dilatometry test provided hardness data according to the cooling rates, which were used to determine the material constants (K 1 to K 5 ) of the QFA and the timetemperature-property diagram of boron steel. Then, FE-simulation of hot stamping was conducted to obtain the cooling curves for blanks with thicknesses of 1.2 and 1.6 mm. The extracted results from the FE-simulation were used to predict the hardness distribution of the hotstamped parts using QFA. Finally, a hot stamping experiment was performed to verify the predicted results and to examine the effect of the blank thickness on the cooling rates of a hotstamped part. The predicted hardnesses for the parts were in good agreement with the measured values, within a maximum error of 4.96 pct.

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“…For the real quenchant, constant effective values of film coefficient/heat transfer coefficient (h) are assumed. The data of selected quenchants as per standard literature [37] including effective film coefficient (h) and corresponding severity of quench/ Grossmann number (H) are presented in Table 1.…”

Section: Modeling Workmentioning

confidence: 99%

A Pure Implicit Finite Difference-Based Modeling Approach for Prediction of the Hardenability of Eutectoid Steel

Singh

Mondal

Mishra

et al. 2014

Metallogr. Microstruct. Anal.

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In this work, an independent pure implicit finite difference-based modeling approach has been adopted for the determination of the hardenability of eutectoid steel. In this model, cooling curves were generated by solving transient heat transfer equations through discretization with pure implicit finite difference scheme in view of constant effective thermophysical properties of AISI-1080 steel. The cooling curves were solved against the 50% transformation nose of the time-temperature-transformation diagram in order to predict hardening behavior of AISI-1080 steel in terms of hardenability parameters (Grossmann critical diameter, D C ; and ideal critical diameter, D I ) and the variation of the ratio of the unhardened core diameter (D u ) to diameter of steel bar (D). Furthermore, a relationship is established between the Grossmann critical diameter and the heat transfer coefficient. The hardenability predicted by the developed model was found to match reasonably with that obtained through the chemical composition method. Therefore, the model developed in the present work can be used for direct determination of D C , D I , and D u without resorting to any rigorous experimentation.

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Predicting properties and minimizing residual stress in quenched steel parts

Cited by 33 publications

References 7 publications

Improvements in quench factor modelling

Improvements in quench factor modelling

Novel Method for Predicting Hardness Distribution of Hot-Stamped Part Using FE-Simulation Coupled with Quench Factor Analysis

A Pure Implicit Finite Difference-Based Modeling Approach for Prediction of the Hardenability of Eutectoid Steel

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