Microstructural modelling in metals processing

Grong, Ø.; Shercliff, Hugh

doi:10.1016/s0079-6425(00)00004-9

Cited by 191 publications

(117 citation statements)

References 95 publications

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“…The additivity rule is only strictly valid under certain conditions Grong and Shercliff 2002). first referred to an isokinetic transformation as one where nucleation rate and growth rate are proportional over the considered temperature range.…”

Section: Transforming Isothermal Model To Varying Temperaturementioning

confidence: 99%

“…Particular applications, alternatives to the additivity rule and discussions on its validity can be found in Kuban et al (1986); Lusk and Jou (1997); Reti and Felde (1999); Todinov (1998); Zhu et al (1997). Grong and Shercliff (2002) show, when modelling welding, that the results were sufficient accurate applying the additivity principle for calculation of microstructure state variables. One should also bear in mind that experimentally determined data themselves, such as the basic TTTdiagrams, are quite uncertain in that they are highly dependent on exact chemical composition and on phase grain sizes (Callister 2007).…”

Section: Transforming Isothermal Model To Varying Temperaturementioning

confidence: 99%

“…One should also bear in mind that experimentally determined data themselves, such as the basic TTTdiagrams, are quite uncertain in that they are highly dependent on exact chemical composition and on phase grain sizes (Callister 2007). The application of the additivity principle is based on the use of a fictitious time (Denis et al 1992;Grong and Shercliff 2002;Malinov et al 2001a). Demonstration of the use of the additivity rule is presented in Figure 27.…”

Section: Transforming Isothermal Model To Varying Temperaturementioning

confidence: 99%

“…Figure 27. Schematic illustration of the principle of additivity rule, showing the approximation of cooling as the sum of short time step-wise isothermal temperature holding, after (Denis et al 1992;Grong and Shercliff 2002;Malinov et al 2001a). …”

Section: Transforming Isothermal Model To Varying Temperaturementioning

confidence: 99%

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A model for Ti–6Al–4V microstructure evolution for arbitrary temperature changes

Murgau

Pederson

Lindgren

2012

Modelling Simul. Mater. Sci. Eng.

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This thesis is devoted to microstructure modelling of Ti-6Al-4V. The microstructure and the mechanical properties of titanium alloys are highly dependent on the temperature history experienced by the material. The developed microstructure model accounts for thermal driving forces and is applicable for general temperature histories. It has been applied to study wire feed additive manufacturing processes that induce repetitive heating and cooling cycles. The microstructure model adopts internal state variables to represent the microstructure through microstructure constituents' fractions in finite element simulation. This makes it possible to apply the model efficiently for large computational models of general thermomechanical processes. The model is calibrated and validated versus literature data. It is applied to Gas Tungsten Arc Welding -also known as Tungsten Inert Gas welding-wire feed additive manufacturing process. Four quantities are calculated in the model: the volume fraction of phase, consisting of Widmanstätten , grain boundary , and martensite . The phase transformations during cooling are modelled based on diffusional theory described by a Johnson-Mehl-AvramiKolmogorov formulation, except for diffusionless martensite formation where the Koistinen-Marburger equation is used. A parabolic growth rate equation is used for the to transformation upon heating. An added variable, structure size indicator of Widmanstätten , has also been implemented and calibrated. It is written in a simple Arrhenius format. The microstructure model is applied to in finite element simulation of wire feed additive manufacturing. Finally, coupling with a physically based constitutive model enables a comprehensive and predictive model of the properties that evolve during processing.

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Section: Transforming Isothermal Model To Varying Temperaturementioning

confidence: 99%

Section: Transforming Isothermal Model To Varying Temperaturementioning

confidence: 99%

Section: Transforming Isothermal Model To Varying Temperaturementioning

confidence: 99%

Section: Transforming Isothermal Model To Varying Temperaturementioning

confidence: 99%

See 2 more Smart Citations

A model for Ti–6Al–4V microstructure evolution for arbitrary temperature changes

Murgau

Pederson

Lindgren

2012

Modelling Simul. Mater. Sci. Eng.

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“…Furthermore, some examples where the microstructural prediction are valuable to control the final properties (e.g., toughness of steel welds); to the knowledge of microstructural limits for process optimization (e.g., maximum welding speed) and when microstructure captures the coupling through multi-stage process, giving opportunities for new alloys and process development (e.g., effect of prior forming and heat treatment on weldability) 1 .…”

Section: Introductionmentioning

confidence: 99%

Numerical Predictions for the Thermal History, Microstructure and Hardness Distributions at the HAZ during Welding of Low Alloy Steels

et al. 2016

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A phenomenological model to predict the multiphase diffusional decomposition of the austenite in low-alloy hypoeutectoid steels was adapted for welding conditions. The kinetics of phase transformations coupled with the heat transfer phenomena was numerically implemented using the Finite Volume Method (FVM) in a computational code. The model was applied to simulate the welding of a commercial type of low-alloy hypoeutectoid steel, making it possible to track the phase formations and to predict the volume fractions of ferrite, pearlite and bainite at the heat-affected zone (HAZ). The volume fraction of martensite was calculated using a novel kinetic model based on the optimization of the well-known Koistinen-Marburger model. Results were confronted with the predictions provided by the continuous cooling transformation (CCT) diagram for the investigated steel, allowing the use of the proposed methodology for the microstructure and hardness predictions at the HAZ of low-alloy hypoeutectoid steels.

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