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Simulation of ingot casting processes at DeutscheEdelstahlwerke GmbHreg

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Simulation of ingot casting processes at Deutsche Edelstahlwerke GmbHreg

L Hartmann1 C Ernst1 and J-S Klung1 1 Deutsche Edelstahlwerke GmbHreg Auestraszlige 4 Witten Germany

E-mail larshartmanndew-stahlcom

Abstract To enhance the quality of tool steels it is necessary to analyse all stages of the production process During the ingot- or continuous casting processes and the following solidification material and geometry depending reactions cause defects such as macro segregations or porosities In former times the trial and error approach together with the experience and creativity of the steelworks engineers was used to improve the as-cast quality with a high amount of test procedures and a high demand of research time and costs Further development in software and algorithms has allowed modern simulation techniques to find their way into industrial steel production and casting-simulations are widely used to achieve an accurate prediction of the ingot quality To improve the as-cast quality several ingot casting processes of tool steels were studied at the RampD department of Deutsche Edelstahlwerke GmbH by using the numerical casting simulation software MAGMASOFTreg In this paper some results extracted from the simulation software are shown and compared to experimental investigations

1 Introduction In former times it was usual to develop new alloys or process routes by a trial and error approach Nowadays the state of the art is to analyse processes with simulations Combining numerical simulation with experimental investigations leads the development of processes to a higher flexibility with lower costs As for this reason Deutsche Edelstahlwerke GmbHreg implemented MAGMASOFTreg to investigate the ingot casting process In steel industry ingot casting is still used especially for high-alloyed or large forging steel grades Hence the optimization of ingot casting is necessary although most of the steel is produced via continuous casting For the physical models used in process simulation various thermo-physical properties have to be known In the past databases for binary systems were created first for high and then for low alloyed steel grades But the complex interactions of physics in high-alloyed steels avoided a numerical simulation for casting processes Since new programs like Thermo-Calcreg and JMatProreg were developed it is possible to calculate the necessary data for the simulation The thermo-physical properties in addition to further process parameters are needed Also the geometry has to be created which is supposed to be investigated [1-4]

In this paper results from an experimental investigation are compared with results from a numerical simulation The investigated bottom casted ingot was made of corrosion resistant plastic tool steel with a weight of 15t For the plastic tool steel criteria as hardness or compression strength wear and corrosion resistance optimal thermal conductivity and long tool life is of most importance On the other hand the mould-producing industry demands a good machinability polishability etchability weldability and in case of heat treatment a low tendency for distortion to reduce production costs and

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

Published under licence by IOP Publishing Ltd 1

the effort of post treatment processes For this reason the solidification structure is very important to improve the steel quality The necessary material data was calculated by the programs Thermo-Calcreg DICTRAregtrade and JMatProreg and geometry was constructed with SolidWorksreg

2 Preparation To verify the accuracy of simulation it is necessary to investigate a real process which is identical with the simulation setup Of course time and effort for the experimental investigation are much higher then for the numerical simulation Therefore a well known ingot casting process was taken for verification

Figure 1 Sampling of the 15 t ingot for the investigations

21 Experimental investigation A bottom-casted ingot out of a double ingot group was investigated The calculated chemical composition of the examined ingot is similar to the well known plastic mould steel X33CrS16 with some modifications Mainly carbon and chromium contents were lowered and sulphur was raised The weight of one of the ingots was 15t and the teeming temperature after ladle treatment was about 1536degC Both ingots were filled within 16 minutes and stripped after five hours with a measured surface temperature of about 721degC The height of the ingots was about 2500mm and the width was twice as large as the thickness The initial mould temperature was assumed to be 50degC as it is usual for multiple casting processes For heat transfer coefficients between mould insulation material and ingot temperature depending data was used

For further investigations the ingot was sampled into three horizontal regions bottom middle and top as well as in two longitudinal regions as seen in figure 1 From these sample plates etch and sulphur prints have been made to characterize macro segregations and structures Also spectrometric measurements and microprobe analysis were made to verify the calculation results

22 Simulation At first the geometry used in the steelworks was reproduced via CAD-model Then the data set for all components and their interactions were created as they werenrsquot available in the MAGMASOFTreg

top

bottom

spectrometricmeasurements

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

2

database It is for numerical simulation the most important step to define all the interactions and material data correctly

One of the most important aspects for a realistic and reliable process simulation is the use of appropriate data for all components and their interactions Thereby the group teeming bottom plate the complete mould the centre runner gating insulation materials the ingot with hot top or feeder head and down sprue the layer of covering powder on top of the feeder head and all interfaces between mould melt and insulation material are taken into account For the feeder and gating system reliable coefficients are included in the software database and approved by several investigations in the foundry industry (as i e the heat transfer coefficient conductivity and density) The covering powder is considered as a region with a specific heat transfer regarding the required characteristics For the calculation thermo-physical properties of the cast material the commercial software Thermo-Calcreg DICTRAregtrade and JMatProreg were used JMatProreg calculates temperature depending properties like density specific heat capacity and thermal conductivity to name only three of the required material data The liquidus temperature of the multi-component system was determined to be nearly 1500degC by JMatProreg It should be pointed out that calculations for high-alloyed steel grades are still imprecise [5] As for this reason qualitative measurements up to a temperature of 1250degC for the investigated steel were done In figure 2 the calculated and measured values are compared The diagram shows that the marked values do not differ substantially although the gradient of calculation and measurement is different

In the simulation thermo-solutal convection of the elements were considered to predict segregation [6] The thermal expansion coefficient describes in combination with the solute expansion coefficient the change in density as a function of temperature for each element Furthermore partition coefficients of all elements contained in the alloy were calculated with Thermo-Calcreg and diffusion coefficients with DICTRAregtrade Most important for the reliability of simulation results is the partition coefficient of all elements which is described as the concentration of the element in the solid phase divided by its concentration in the melt at the interphase zone between solid and liquid

K=CsCl (1)

cp [J(gK)] calculated cp measured[J(gK)]density[gcmsup3] calculated densitymeasured[gcmsup3]conductivitycalculated[W(mK)] conductivitymeasured[W(mK)]

[J(g

K)]

400

600

800

100 300 400 650 850 1050 1250Temperature[deg C]

0

10

20

30

[gc

msup3 a

nd W

(mK

)]

cp [J(gK)] calculated cp measured [J(gK)]density [gcm sup3] calculated density measured[gcmsup3 ]conductivity calculated [W(mK)] conductivity measured [W(mK)]

cp [J(gK)] calculated cp measured[J(gK)]density[gcmsup3] calculated densitymeasured[gcmsup3]conductivitycalculated[W(mK)] conductivitymeasured[W(mK)]

[J(g

K)]

400

600

800

100 300 400 650 850 1050 1250Temperature[deg C]

0

10

20

30

[gc

msup3 a

nd W

(mK

)]

cp [J(gK)] calculated cp measured [J(gK)]density [gcm sup3] calculated density measured[gcmsup3 ]conductivity calculated [W(mK)] conductivity measured [W(mK)]

Figure 2 Calculated and measured thermo-physical properties of the investigated steel

Hence it was possible to simulate the filling as well as the solidification process with MAGMASOFTreg An initial filling simulation is necessary to get the correct temperature profile at solidification Especially for ingot casting the temperature profile is very inhomogeneous after a long filling which influences the results strongly

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

3

3 Results The examined solidification time and temperatures correlate well with the calculated results

Macroscopic examination regarding porosities and segregations are shown in figure 3 The etch prints as well as the sulphur prints (figure 3b and 3c) show pencil like A-segregations which are also predicted by numerical simulation (figure 3a) Porosities are mainly investigated in the centreline from ingot head until 500mm above the bottom The calculated Porosity- and Niyama-criterion strongly suggests the same characteristic (figure 4) For Niyama-criterion the critical value 01 was used because it seems to be more sufficient than the commonly defined value of 07 for steel [7]

The comparison of the calculated and measured macro segregations are presented in figure 5 In the diagrams the concentration of the elements carbon chromium and manganese normalized by the initial concentration is described For manganese the calculated values correlate very well with the measured ones whereas for carbon the value differs clearly at the top of the ingot This difference could be caused by the fact that pickup of carbon from the insulation and the covering powder is not taken into account [8] Also the results for the high-alloyed element chromium varied along the centreline The measurements indicate a positive segregation at the top and at the bottom region For the top region the simulation approves a positive segregation but at the bottom the simulation predicts a negative segregation as would be expected in general [9]

Figure 3 a) Sulphur segregation calculated b) Sulphur prints and c) etch prints

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

4

Figure 4 Niyama-and Porosity-criterion

Figure 5 Vertical segregation coefficients

4 Conclusion Numerical simulation is well able to describe solidification processes Most of the investigated aspects were in good agreement to the simulation Only the prediction of chromium differs clearly Reasonable for this difference could be inaccuracy in measurement and the fact that only one ingot was tested Hence it is necessary to prove or to disprove this phenomenon with further investigations Also thermo-physical data for high-alloyed steel grades is still inexact At higher temperatures the accuracy of the measured values as well as the calculated ones decreases even more For this reason it is difficult to say if the input data represent the reality sufficiently [10]

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

Cmix Cin

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

Cmix Cin

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

Cmix Cin

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

Cmix Cin

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

Cmix Cin

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

Cmix Cin

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

5

Nevertheless simulation offers the possibility to optimize ingot casting processes without risking any material Because of this reason it is highly important for a steel foundry to establish simulation as soon as possible to be in a leading position Additionally simulation will help us to increase our understanding of solidification [11]

References [1] Hahn I 2008 Automatic computerized optimization in die casting processes Casing Plant and

Technology Vol 4 (Aachen) p 2-14 [2] Schaumlfer W Hartmann G Hepp E 2009 Innovative process simulation of tool steel production

processes 8th international Tooling Conference Vol 2 (Aachen) p 751-762 [3] Lagerstedt A Sarnet J Adolfi S Fredriksson H Macrosegregation in Ingot Cast Tool Steel

ISRN KTH-MG-INR-0504SE TRITA-MG 200504 [4] Jolly M 2002 Casting simulation How well do reality and virtual casting match State of the

art review Int J Cast Metals Res 14 p 303-313 [5] Guo Z Saunders N Hepp E Schilleacute J-Ph 2005 Modelling of materials properties ndash a viable

solution to the lack of material data in casting httpwwwsentesoftwarecoukbibliohtml [6] IidaT Guthrie I L R 1988 The physical properties of liquid metals (Oxford Clarendon Press) [7] Carlson K D Beckermann C 2009 Prediction of shrinkage pore volume fraction using a

dimensionless Niyama criterion Metallurgical and Materials Transsactions A Vol 40A p 163-175

[8] Ragnarsson L Ek M Eliasson A Sichen D 2010 Flow pattern in ingot during mould filling and its impact on inclusion removal Ironmaking and Steelmaking Vol 37 No5 p 347-352

[9] Olsson A Some aspects of the formation of macrosegregation and structure in ingots Proceedings of the Royal Institute of Technology Dept of Casting of Metals (Stockholm)

[10] Duh D Ernst C Klung J-S Werner M 2009 Implementation of materials modelling to enhance efficiency in industrial tool steel development 8th international Tooling Conference Aachen Vol 2 p 763-774

[11] Dantzig J A Rappaz M 2009 Solidification First edition (ItalyEPFL Press)

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

6

the effort of post treatment processes For this reason the solidification structure is very important to improve the steel quality The necessary material data was calculated by the programs Thermo-Calcreg DICTRAregtrade and JMatProreg and geometry was constructed with SolidWorksreg

2 Preparation To verify the accuracy of simulation it is necessary to investigate a real process which is identical with the simulation setup Of course time and effort for the experimental investigation are much higher then for the numerical simulation Therefore a well known ingot casting process was taken for verification

Figure 1 Sampling of the 15 t ingot for the investigations

21 Experimental investigation A bottom-casted ingot out of a double ingot group was investigated The calculated chemical composition of the examined ingot is similar to the well known plastic mould steel X33CrS16 with some modifications Mainly carbon and chromium contents were lowered and sulphur was raised The weight of one of the ingots was 15t and the teeming temperature after ladle treatment was about 1536degC Both ingots were filled within 16 minutes and stripped after five hours with a measured surface temperature of about 721degC The height of the ingots was about 2500mm and the width was twice as large as the thickness The initial mould temperature was assumed to be 50degC as it is usual for multiple casting processes For heat transfer coefficients between mould insulation material and ingot temperature depending data was used

For further investigations the ingot was sampled into three horizontal regions bottom middle and top as well as in two longitudinal regions as seen in figure 1 From these sample plates etch and sulphur prints have been made to characterize macro segregations and structures Also spectrometric measurements and microprobe analysis were made to verify the calculation results

22 Simulation At first the geometry used in the steelworks was reproduced via CAD-model Then the data set for all components and their interactions were created as they werenrsquot available in the MAGMASOFTreg

top

bottom

spectrometricmeasurements

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

2

database It is for numerical simulation the most important step to define all the interactions and material data correctly

One of the most important aspects for a realistic and reliable process simulation is the use of appropriate data for all components and their interactions Thereby the group teeming bottom plate the complete mould the centre runner gating insulation materials the ingot with hot top or feeder head and down sprue the layer of covering powder on top of the feeder head and all interfaces between mould melt and insulation material are taken into account For the feeder and gating system reliable coefficients are included in the software database and approved by several investigations in the foundry industry (as i e the heat transfer coefficient conductivity and density) The covering powder is considered as a region with a specific heat transfer regarding the required characteristics For the calculation thermo-physical properties of the cast material the commercial software Thermo-Calcreg DICTRAregtrade and JMatProreg were used JMatProreg calculates temperature depending properties like density specific heat capacity and thermal conductivity to name only three of the required material data The liquidus temperature of the multi-component system was determined to be nearly 1500degC by JMatProreg It should be pointed out that calculations for high-alloyed steel grades are still imprecise [5] As for this reason qualitative measurements up to a temperature of 1250degC for the investigated steel were done In figure 2 the calculated and measured values are compared The diagram shows that the marked values do not differ substantially although the gradient of calculation and measurement is different

In the simulation thermo-solutal convection of the elements were considered to predict segregation [6] The thermal expansion coefficient describes in combination with the solute expansion coefficient the change in density as a function of temperature for each element Furthermore partition coefficients of all elements contained in the alloy were calculated with Thermo-Calcreg and diffusion coefficients with DICTRAregtrade Most important for the reliability of simulation results is the partition coefficient of all elements which is described as the concentration of the element in the solid phase divided by its concentration in the melt at the interphase zone between solid and liquid

K=CsCl (1)

cp [J(gK)] calculated cp measured[J(gK)]density[gcmsup3] calculated densitymeasured[gcmsup3]conductivitycalculated[W(mK)] conductivitymeasured[W(mK)]

[J(g

K)]

400

600

800

100 300 400 650 850 1050 1250Temperature[deg C]

0

10

20

30

[gc

msup3 a

nd W

(mK

)]

cp [J(gK)] calculated cp measured [J(gK)]density [gcm sup3] calculated density measured[gcmsup3 ]conductivity calculated [W(mK)] conductivity measured [W(mK)]

cp [J(gK)] calculated cp measured[J(gK)]density[gcmsup3] calculated densitymeasured[gcmsup3]conductivitycalculated[W(mK)] conductivitymeasured[W(mK)]

[J(g

K)]

400

600

800

100 300 400 650 850 1050 1250Temperature[deg C]

0

10

20

30

[gc

msup3 a

nd W

(mK

)]

cp [J(gK)] calculated cp measured [J(gK)]density [gcm sup3] calculated density measured[gcmsup3 ]conductivity calculated [W(mK)] conductivity measured [W(mK)]

Figure 2 Calculated and measured thermo-physical properties of the investigated steel

Hence it was possible to simulate the filling as well as the solidification process with MAGMASOFTreg An initial filling simulation is necessary to get the correct temperature profile at solidification Especially for ingot casting the temperature profile is very inhomogeneous after a long filling which influences the results strongly

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

3

3 Results The examined solidification time and temperatures correlate well with the calculated results

Macroscopic examination regarding porosities and segregations are shown in figure 3 The etch prints as well as the sulphur prints (figure 3b and 3c) show pencil like A-segregations which are also predicted by numerical simulation (figure 3a) Porosities are mainly investigated in the centreline from ingot head until 500mm above the bottom The calculated Porosity- and Niyama-criterion strongly suggests the same characteristic (figure 4) For Niyama-criterion the critical value 01 was used because it seems to be more sufficient than the commonly defined value of 07 for steel [7]

The comparison of the calculated and measured macro segregations are presented in figure 5 In the diagrams the concentration of the elements carbon chromium and manganese normalized by the initial concentration is described For manganese the calculated values correlate very well with the measured ones whereas for carbon the value differs clearly at the top of the ingot This difference could be caused by the fact that pickup of carbon from the insulation and the covering powder is not taken into account [8] Also the results for the high-alloyed element chromium varied along the centreline The measurements indicate a positive segregation at the top and at the bottom region For the top region the simulation approves a positive segregation but at the bottom the simulation predicts a negative segregation as would be expected in general [9]

Figure 3 a) Sulphur segregation calculated b) Sulphur prints and c) etch prints

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

4

Figure 4 Niyama-and Porosity-criterion

Figure 5 Vertical segregation coefficients

4 Conclusion Numerical simulation is well able to describe solidification processes Most of the investigated aspects were in good agreement to the simulation Only the prediction of chromium differs clearly Reasonable for this difference could be inaccuracy in measurement and the fact that only one ingot was tested Hence it is necessary to prove or to disprove this phenomenon with further investigations Also thermo-physical data for high-alloyed steel grades is still inexact At higher temperatures the accuracy of the measured values as well as the calculated ones decreases even more For this reason it is difficult to say if the input data represent the reality sufficiently [10]

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

Cmix Cin

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

Cmix Cin

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

Cmix Cin

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

Cmix Cin

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

Cmix Cin

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

Cmix Cin

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

5

Nevertheless simulation offers the possibility to optimize ingot casting processes without risking any material Because of this reason it is highly important for a steel foundry to establish simulation as soon as possible to be in a leading position Additionally simulation will help us to increase our understanding of solidification [11]

References [1] Hahn I 2008 Automatic computerized optimization in die casting processes Casing Plant and

Technology Vol 4 (Aachen) p 2-14 [2] Schaumlfer W Hartmann G Hepp E 2009 Innovative process simulation of tool steel production

processes 8th international Tooling Conference Vol 2 (Aachen) p 751-762 [3] Lagerstedt A Sarnet J Adolfi S Fredriksson H Macrosegregation in Ingot Cast Tool Steel

ISRN KTH-MG-INR-0504SE TRITA-MG 200504 [4] Jolly M 2002 Casting simulation How well do reality and virtual casting match State of the

art review Int J Cast Metals Res 14 p 303-313 [5] Guo Z Saunders N Hepp E Schilleacute J-Ph 2005 Modelling of materials properties ndash a viable

solution to the lack of material data in casting httpwwwsentesoftwarecoukbibliohtml [6] IidaT Guthrie I L R 1988 The physical properties of liquid metals (Oxford Clarendon Press) [7] Carlson K D Beckermann C 2009 Prediction of shrinkage pore volume fraction using a

dimensionless Niyama criterion Metallurgical and Materials Transsactions A Vol 40A p 163-175

[8] Ragnarsson L Ek M Eliasson A Sichen D 2010 Flow pattern in ingot during mould filling and its impact on inclusion removal Ironmaking and Steelmaking Vol 37 No5 p 347-352

[9] Olsson A Some aspects of the formation of macrosegregation and structure in ingots Proceedings of the Royal Institute of Technology Dept of Casting of Metals (Stockholm)

[10] Duh D Ernst C Klung J-S Werner M 2009 Implementation of materials modelling to enhance efficiency in industrial tool steel development 8th international Tooling Conference Aachen Vol 2 p 763-774

[11] Dantzig J A Rappaz M 2009 Solidification First edition (ItalyEPFL Press)

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

6

database It is for numerical simulation the most important step to define all the interactions and material data correctly

One of the most important aspects for a realistic and reliable process simulation is the use of appropriate data for all components and their interactions Thereby the group teeming bottom plate the complete mould the centre runner gating insulation materials the ingot with hot top or feeder head and down sprue the layer of covering powder on top of the feeder head and all interfaces between mould melt and insulation material are taken into account For the feeder and gating system reliable coefficients are included in the software database and approved by several investigations in the foundry industry (as i e the heat transfer coefficient conductivity and density) The covering powder is considered as a region with a specific heat transfer regarding the required characteristics For the calculation thermo-physical properties of the cast material the commercial software Thermo-Calcreg DICTRAregtrade and JMatProreg were used JMatProreg calculates temperature depending properties like density specific heat capacity and thermal conductivity to name only three of the required material data The liquidus temperature of the multi-component system was determined to be nearly 1500degC by JMatProreg It should be pointed out that calculations for high-alloyed steel grades are still imprecise [5] As for this reason qualitative measurements up to a temperature of 1250degC for the investigated steel were done In figure 2 the calculated and measured values are compared The diagram shows that the marked values do not differ substantially although the gradient of calculation and measurement is different

In the simulation thermo-solutal convection of the elements were considered to predict segregation [6] The thermal expansion coefficient describes in combination with the solute expansion coefficient the change in density as a function of temperature for each element Furthermore partition coefficients of all elements contained in the alloy were calculated with Thermo-Calcreg and diffusion coefficients with DICTRAregtrade Most important for the reliability of simulation results is the partition coefficient of all elements which is described as the concentration of the element in the solid phase divided by its concentration in the melt at the interphase zone between solid and liquid

K=CsCl (1)

cp [J(gK)] calculated cp measured[J(gK)]density[gcmsup3] calculated densitymeasured[gcmsup3]conductivitycalculated[W(mK)] conductivitymeasured[W(mK)]

[J(g

K)]

400

600

800

100 300 400 650 850 1050 1250Temperature[deg C]

0

10

20

30

[gc

msup3 a

nd W

(mK

)]

cp [J(gK)] calculated cp measured [J(gK)]density [gcm sup3] calculated density measured[gcmsup3 ]conductivity calculated [W(mK)] conductivity measured [W(mK)]

cp [J(gK)] calculated cp measured[J(gK)]density[gcmsup3] calculated densitymeasured[gcmsup3]conductivitycalculated[W(mK)] conductivitymeasured[W(mK)]

[J(g

K)]

400

600

800

100 300 400 650 850 1050 1250Temperature[deg C]

0

10

20

30

[gc

msup3 a

nd W

(mK

)]

cp [J(gK)] calculated cp measured [J(gK)]density [gcm sup3] calculated density measured[gcmsup3 ]conductivity calculated [W(mK)] conductivity measured [W(mK)]

Figure 2 Calculated and measured thermo-physical properties of the investigated steel

Hence it was possible to simulate the filling as well as the solidification process with MAGMASOFTreg An initial filling simulation is necessary to get the correct temperature profile at solidification Especially for ingot casting the temperature profile is very inhomogeneous after a long filling which influences the results strongly

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

3

3 Results The examined solidification time and temperatures correlate well with the calculated results

Macroscopic examination regarding porosities and segregations are shown in figure 3 The etch prints as well as the sulphur prints (figure 3b and 3c) show pencil like A-segregations which are also predicted by numerical simulation (figure 3a) Porosities are mainly investigated in the centreline from ingot head until 500mm above the bottom The calculated Porosity- and Niyama-criterion strongly suggests the same characteristic (figure 4) For Niyama-criterion the critical value 01 was used because it seems to be more sufficient than the commonly defined value of 07 for steel [7]

The comparison of the calculated and measured macro segregations are presented in figure 5 In the diagrams the concentration of the elements carbon chromium and manganese normalized by the initial concentration is described For manganese the calculated values correlate very well with the measured ones whereas for carbon the value differs clearly at the top of the ingot This difference could be caused by the fact that pickup of carbon from the insulation and the covering powder is not taken into account [8] Also the results for the high-alloyed element chromium varied along the centreline The measurements indicate a positive segregation at the top and at the bottom region For the top region the simulation approves a positive segregation but at the bottom the simulation predicts a negative segregation as would be expected in general [9]

Figure 3 a) Sulphur segregation calculated b) Sulphur prints and c) etch prints

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

4

Figure 4 Niyama-and Porosity-criterion

Figure 5 Vertical segregation coefficients

4 Conclusion Numerical simulation is well able to describe solidification processes Most of the investigated aspects were in good agreement to the simulation Only the prediction of chromium differs clearly Reasonable for this difference could be inaccuracy in measurement and the fact that only one ingot was tested Hence it is necessary to prove or to disprove this phenomenon with further investigations Also thermo-physical data for high-alloyed steel grades is still inexact At higher temperatures the accuracy of the measured values as well as the calculated ones decreases even more For this reason it is difficult to say if the input data represent the reality sufficiently [10]

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

Cmix Cin

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

Cmix Cin

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

Cmix Cin

Carbon (vertical)

250

1000

1750

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0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

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Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

Cmix Cin

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

Cmix Cin

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

5

Nevertheless simulation offers the possibility to optimize ingot casting processes without risking any material Because of this reason it is highly important for a steel foundry to establish simulation as soon as possible to be in a leading position Additionally simulation will help us to increase our understanding of solidification [11]

References [1] Hahn I 2008 Automatic computerized optimization in die casting processes Casing Plant and

Technology Vol 4 (Aachen) p 2-14 [2] Schaumlfer W Hartmann G Hepp E 2009 Innovative process simulation of tool steel production

processes 8th international Tooling Conference Vol 2 (Aachen) p 751-762 [3] Lagerstedt A Sarnet J Adolfi S Fredriksson H Macrosegregation in Ingot Cast Tool Steel

ISRN KTH-MG-INR-0504SE TRITA-MG 200504 [4] Jolly M 2002 Casting simulation How well do reality and virtual casting match State of the

art review Int J Cast Metals Res 14 p 303-313 [5] Guo Z Saunders N Hepp E Schilleacute J-Ph 2005 Modelling of materials properties ndash a viable

solution to the lack of material data in casting httpwwwsentesoftwarecoukbibliohtml [6] IidaT Guthrie I L R 1988 The physical properties of liquid metals (Oxford Clarendon Press) [7] Carlson K D Beckermann C 2009 Prediction of shrinkage pore volume fraction using a

dimensionless Niyama criterion Metallurgical and Materials Transsactions A Vol 40A p 163-175

[8] Ragnarsson L Ek M Eliasson A Sichen D 2010 Flow pattern in ingot during mould filling and its impact on inclusion removal Ironmaking and Steelmaking Vol 37 No5 p 347-352

[9] Olsson A Some aspects of the formation of macrosegregation and structure in ingots Proceedings of the Royal Institute of Technology Dept of Casting of Metals (Stockholm)

[10] Duh D Ernst C Klung J-S Werner M 2009 Implementation of materials modelling to enhance efficiency in industrial tool steel development 8th international Tooling Conference Aachen Vol 2 p 763-774

[11] Dantzig J A Rappaz M 2009 Solidification First edition (ItalyEPFL Press)

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

6

3 Results The examined solidification time and temperatures correlate well with the calculated results

Macroscopic examination regarding porosities and segregations are shown in figure 3 The etch prints as well as the sulphur prints (figure 3b and 3c) show pencil like A-segregations which are also predicted by numerical simulation (figure 3a) Porosities are mainly investigated in the centreline from ingot head until 500mm above the bottom The calculated Porosity- and Niyama-criterion strongly suggests the same characteristic (figure 4) For Niyama-criterion the critical value 01 was used because it seems to be more sufficient than the commonly defined value of 07 for steel [7]

The comparison of the calculated and measured macro segregations are presented in figure 5 In the diagrams the concentration of the elements carbon chromium and manganese normalized by the initial concentration is described For manganese the calculated values correlate very well with the measured ones whereas for carbon the value differs clearly at the top of the ingot This difference could be caused by the fact that pickup of carbon from the insulation and the covering powder is not taken into account [8] Also the results for the high-alloyed element chromium varied along the centreline The measurements indicate a positive segregation at the top and at the bottom region For the top region the simulation approves a positive segregation but at the bottom the simulation predicts a negative segregation as would be expected in general [9]

Figure 3 a) Sulphur segregation calculated b) Sulphur prints and c) etch prints

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

4

Figure 4 Niyama-and Porosity-criterion

Figure 5 Vertical segregation coefficients

4 Conclusion Numerical simulation is well able to describe solidification processes Most of the investigated aspects were in good agreement to the simulation Only the prediction of chromium differs clearly Reasonable for this difference could be inaccuracy in measurement and the fact that only one ingot was tested Hence it is necessary to prove or to disprove this phenomenon with further investigations Also thermo-physical data for high-alloyed steel grades is still inexact At higher temperatures the accuracy of the measured values as well as the calculated ones decreases even more For this reason it is difficult to say if the input data represent the reality sufficiently [10]

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

Cmix Cin

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

Cmix Cin

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

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ght[

mm

]

measuredC initialsimulation

Cmix Cin

Carbon (vertical)

250

1000

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0 05 1 15 2 25

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ght[

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]

measuredC initialsimulation

measuredC initialsimulation

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250

1000

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0 05 1 15 2 25

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ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

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Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

Cmix Cin

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

5

Nevertheless simulation offers the possibility to optimize ingot casting processes without risking any material Because of this reason it is highly important for a steel foundry to establish simulation as soon as possible to be in a leading position Additionally simulation will help us to increase our understanding of solidification [11]

References [1] Hahn I 2008 Automatic computerized optimization in die casting processes Casing Plant and

Technology Vol 4 (Aachen) p 2-14 [2] Schaumlfer W Hartmann G Hepp E 2009 Innovative process simulation of tool steel production

processes 8th international Tooling Conference Vol 2 (Aachen) p 751-762 [3] Lagerstedt A Sarnet J Adolfi S Fredriksson H Macrosegregation in Ingot Cast Tool Steel

ISRN KTH-MG-INR-0504SE TRITA-MG 200504 [4] Jolly M 2002 Casting simulation How well do reality and virtual casting match State of the

art review Int J Cast Metals Res 14 p 303-313 [5] Guo Z Saunders N Hepp E Schilleacute J-Ph 2005 Modelling of materials properties ndash a viable

solution to the lack of material data in casting httpwwwsentesoftwarecoukbibliohtml [6] IidaT Guthrie I L R 1988 The physical properties of liquid metals (Oxford Clarendon Press) [7] Carlson K D Beckermann C 2009 Prediction of shrinkage pore volume fraction using a

dimensionless Niyama criterion Metallurgical and Materials Transsactions A Vol 40A p 163-175

[8] Ragnarsson L Ek M Eliasson A Sichen D 2010 Flow pattern in ingot during mould filling and its impact on inclusion removal Ironmaking and Steelmaking Vol 37 No5 p 347-352

[9] Olsson A Some aspects of the formation of macrosegregation and structure in ingots Proceedings of the Royal Institute of Technology Dept of Casting of Metals (Stockholm)

[10] Duh D Ernst C Klung J-S Werner M 2009 Implementation of materials modelling to enhance efficiency in industrial tool steel development 8th international Tooling Conference Aachen Vol 2 p 763-774

[11] Dantzig J A Rappaz M 2009 Solidification First edition (ItalyEPFL Press)

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

6

Figure 4 Niyama-and Porosity-criterion

Figure 5 Vertical segregation coefficients

4 Conclusion Numerical simulation is well able to describe solidification processes Most of the investigated aspects were in good agreement to the simulation Only the prediction of chromium differs clearly Reasonable for this difference could be inaccuracy in measurement and the fact that only one ingot was tested Hence it is necessary to prove or to disprove this phenomenon with further investigations Also thermo-physical data for high-alloyed steel grades is still inexact At higher temperatures the accuracy of the measured values as well as the calculated ones decreases even more For this reason it is difficult to say if the input data represent the reality sufficiently [10]

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

Cmix Cin

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

Cmix Cin

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Manganese (vertical)

08 09 1 11 12Cmix Cin

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Chromium (vertical)

1 105 11Cmix Cin

09 095

measuredC initialsimulation

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

Cmix Cin

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

Cmix Cin

Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

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Carbon (vertical)

250

1000

1750

2500

0 05 1 15 2 25

Hei

ght[

mm

]

measuredC initialsimulation

measuredC initialsimulation

Cmix Cin

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

5

Nevertheless simulation offers the possibility to optimize ingot casting processes without risking any material Because of this reason it is highly important for a steel foundry to establish simulation as soon as possible to be in a leading position Additionally simulation will help us to increase our understanding of solidification [11]

References [1] Hahn I 2008 Automatic computerized optimization in die casting processes Casing Plant and

Technology Vol 4 (Aachen) p 2-14 [2] Schaumlfer W Hartmann G Hepp E 2009 Innovative process simulation of tool steel production

processes 8th international Tooling Conference Vol 2 (Aachen) p 751-762 [3] Lagerstedt A Sarnet J Adolfi S Fredriksson H Macrosegregation in Ingot Cast Tool Steel

ISRN KTH-MG-INR-0504SE TRITA-MG 200504 [4] Jolly M 2002 Casting simulation How well do reality and virtual casting match State of the

art review Int J Cast Metals Res 14 p 303-313 [5] Guo Z Saunders N Hepp E Schilleacute J-Ph 2005 Modelling of materials properties ndash a viable

solution to the lack of material data in casting httpwwwsentesoftwarecoukbibliohtml [6] IidaT Guthrie I L R 1988 The physical properties of liquid metals (Oxford Clarendon Press) [7] Carlson K D Beckermann C 2009 Prediction of shrinkage pore volume fraction using a

dimensionless Niyama criterion Metallurgical and Materials Transsactions A Vol 40A p 163-175

[8] Ragnarsson L Ek M Eliasson A Sichen D 2010 Flow pattern in ingot during mould filling and its impact on inclusion removal Ironmaking and Steelmaking Vol 37 No5 p 347-352

[9] Olsson A Some aspects of the formation of macrosegregation and structure in ingots Proceedings of the Royal Institute of Technology Dept of Casting of Metals (Stockholm)

[10] Duh D Ernst C Klung J-S Werner M 2009 Implementation of materials modelling to enhance efficiency in industrial tool steel development 8th international Tooling Conference Aachen Vol 2 p 763-774

[11] Dantzig J A Rappaz M 2009 Solidification First edition (ItalyEPFL Press)

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

6

Nevertheless simulation offers the possibility to optimize ingot casting processes without risking any material Because of this reason it is highly important for a steel foundry to establish simulation as soon as possible to be in a leading position Additionally simulation will help us to increase our understanding of solidification [11]

References [1] Hahn I 2008 Automatic computerized optimization in die casting processes Casing Plant and

Technology Vol 4 (Aachen) p 2-14 [2] Schaumlfer W Hartmann G Hepp E 2009 Innovative process simulation of tool steel production

processes 8th international Tooling Conference Vol 2 (Aachen) p 751-762 [3] Lagerstedt A Sarnet J Adolfi S Fredriksson H Macrosegregation in Ingot Cast Tool Steel

ISRN KTH-MG-INR-0504SE TRITA-MG 200504 [4] Jolly M 2002 Casting simulation How well do reality and virtual casting match State of the

art review Int J Cast Metals Res 14 p 303-313 [5] Guo Z Saunders N Hepp E Schilleacute J-Ph 2005 Modelling of materials properties ndash a viable

solution to the lack of material data in casting httpwwwsentesoftwarecoukbibliohtml [6] IidaT Guthrie I L R 1988 The physical properties of liquid metals (Oxford Clarendon Press) [7] Carlson K D Beckermann C 2009 Prediction of shrinkage pore volume fraction using a

dimensionless Niyama criterion Metallurgical and Materials Transsactions A Vol 40A p 163-175

[8] Ragnarsson L Ek M Eliasson A Sichen D 2010 Flow pattern in ingot during mould filling and its impact on inclusion removal Ironmaking and Steelmaking Vol 37 No5 p 347-352

[9] Olsson A Some aspects of the formation of macrosegregation and structure in ingots Proceedings of the Royal Institute of Technology Dept of Casting of Metals (Stockholm)

[10] Duh D Ernst C Klung J-S Werner M 2009 Implementation of materials modelling to enhance efficiency in industrial tool steel development 8th international Tooling Conference Aachen Vol 2 p 763-774

[11] Dantzig J A Rappaz M 2009 Solidification First edition (ItalyEPFL Press)

The 3rd International Conference on Advances in Solidification Processes IOP PublishingIOP Conf Series Materials Science and Engineering 27 (2011) 012063 doi1010881757-899X271012063

6