New approach for the bainite start temperature calculation in steels

García‐Mateo, C.; Sourmail, Thomas; Caballero, Francisca G.; Capdevila, Carlos; Andrés, Carlos García de

doi:10.1179/174328405x51622

Cited by 26 publications

(18 citation statements)

References 23 publications

(30 reference statements)

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“…Cr and Mo influence the phase transformation, since they are [23]. In this study, a variation in the concentrations of Mo and Cr was observed across the boundary between the dendrite core and the interdendritic region; B S and M S temperatures in the interdendritic region can therefore be expected to differ from those in dendritic core region.…”

Section: Resultsmentioning

confidence: 86%

Microstructural Evolution of a Continuously Cooled Air Hardening Steel

Brunelli

Bassani²,

Lecis

et al. 2013

Metallogr. Microstruct. Anal.

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This study studied the phase transformations occurring at different continuous cooling rates in an air hardening steel used in the rock-crushing industry. Samples of this steel were submitted for calorimetric testing using the differential scanning calorimetry (DSC) technique. In the experimental run, the samples were heated at the rate of 0.33°C/s from 50 to 1050°C and equilibrated at this temperature for 900 s, then cooled at seven different cooling rates between 0.05 and 0.5°C/s. For all the cooling rates, the DSC traces of the samples showed a first exothermic peak at about 500°C and for samples cooled at rates higher than 0.15°C/s, a second exothermic peak at about 295°C was observed. From the microstructural investigations carried out by light microscopy (LM) and scanning electron microscopy (SEM), it was observed that all the samples after DSC treatment were characterized by the presence of bainite. In the samples cooled at rates higher than 0.15°C/s, martensite was also detected. Comparing the results of DSC and SEM, it was concluded that the first peak at 500°C corresponds to the austenite ? bainite transformation, while the second peak at 295°C corresponds to the austenite ? martensite transformation. The experimentally determined bainite and martensite start temperatures were compared to the values derived from a number of well-known empirical equations.

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Section: Resultsmentioning

confidence: 86%

Microstructural Evolution of a Continuously Cooled Air Hardening Steel

Brunelli

Bassani²,

Lecis

et al. 2013

Metallogr. Microstruct. Anal.

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“…It is not the intention here to describe the neural network method; details can be found in [9][10][11][12] and the particular method used here has been widely applied in the discovery of phenomena in steels [13][14][15][16][17][18][19][20][21][22]. However, a brief explanation of particular aspects is justified in order to set the scene for later discussions.…”

Section: Modelmentioning

confidence: 99%

Elongation of Irradiated Steels

Forsik

Bhadeshia

2009

Materials and Manufacturing Processes

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A neural network was designed to model the elongation of neutron-irradiated steels. Predictions were compared to experimental values and were in agreement. The model was extrapolated to predict the elongation at high irradiation doses (200 dpa) and high temperatures (750• C). Because of the lack of experimental values at such doses and temperatures, predictions were accompanied with very large modelling uncertainties.

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“…Guo et al 53 have also used an ANN to model the beta transus temperature as a function of alloy chemistry, which showed good agreement with test experimental data and with thermodynamic calculations. Two other papers modelled steel transformation temperatures, the bainite start temperature 54 and the martensitic start temperature 55 respectively using neural networks to model the effect of chemical composition (bainite) or chemical composition and prior austenite grain size (martensite) on the relevant transformation temperature, in these cases a more sophisticated (ANM) empirical modelling approach was being compared with either existing empirical linear regression models or thermodynamic predictions, allowing a degree of physical interpretation.…”

Section: Predictions Related To Thermodynamic Equilibriummentioning

confidence: 99%

Invited review: Adaptive numerical modelling and hybrid physically based ANM approaches in materials engineering – a survey

Reed¹,

Starink²,

Gunn³

et al. 2009

Materials Science and Technology

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Many adaptive numerical modelling (ANM) techniques such as artificial neural networks (including multilayer perceptrons), support vector machines and Gaussian processes have now been applied to a wide range of regression and classification problems in materials science. Materials science offers a wide range of industrial applications and hence problem complexity levels from well physically characterised systems (e.g. high value, low volume products) to high volume low cost applications with intrinsic scatter due to commercial manufacturing processes. The authors review a number of recent examples in the literature, with the aim of identifying best practice in the use of these techniques as part of a multistrand modelling approach. The importance of understanding the basic principles of these modelling techniques and how they can link with other modelling strategies is emphasised. In particular the authors wish to identify the importance of hybrid physically based ANM in taking the field forward, which can range from, at the most basic level, careful data selection and data preprocessing to a full integration of physically based models with advanced ANM. A number of case studies are presented to illustrate the main points of the paper. Keywords: Neural networks, Support vector machines, Gaussian processes, Adaptive numerical modelling, Data driven techniques, Hybrid modelling, Applications in materials science IntroductionThe links between processing variables, microstructure evolution, resultant properties and hence mechanical performance are core tenets of materials engineering and a variety of modelling approaches have been applied to these materials modelling challenges. In some cases, a clear physical basis for such models has now been established, but often these can apply only to well defined conditions, which may not be representative of either genuine industrial (scaled up) production processes, or materials performance under realistic service conditions. The advantages of physically based models are:(i) they are robust to interpolation and extrapolation (as long as the underlying physical mechanisms being modelled are in operation) (ii) they should not require large amounts of data, as typically they only have a limited number of fitted parameters; ideally, a physically based model may have no fitted parameters (iii) the mechanistic verifications derived from physically based modelling may also offer insights into novel materials optimisation strategies (iv) a physically based model is relatively transparent, i.e. the mathematical underlying relationships between input and output parameters may be written in a reasonably clear mathematical form: this is part of a loosely defined group of methods which the authors term 'white box'. However, in a truly integrated processing-performance model, with the levels of uncertainty and complexity associated with industrial practice and complex service conditions, expressing these interlinked processes purely on physics based principles is unlikely to be feasible. ...

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New approach for the bainite start temperature calculation in steels

Cited by 26 publications

References 23 publications

Microstructural Evolution of a Continuously Cooled Air Hardening Steel

Microstructural Evolution of a Continuously Cooled Air Hardening Steel

Elongation of Irradiated Steels

Invited review: Adaptive numerical modelling and hybrid physically based ANM approaches in materials engineering – a survey

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