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Alloy Dependence of the Diffusion Coefficient of Carbon in Austenite and Analysis of Carburization Profiles in Case Hardening of Steels

J. Gegner1,a, A. A. Vasilyev2,b, P.-J. Wilbrandt3,c and M. Kaffenberger1,d 1University of Siegen, Institute of Material Science, Paul-Bonatz-Straße 9-11, 57068 Siegen,

Germany

2St. Petersburg State Polytechnical University, Polytekhnicheskaya 29, 195251, St. Petersburg, Russia

3University of Göttingen, Institute of Material Physics, Friedrich-Hund-Platz 1, 37073 Göttingen, Germany

ajuergen.gegner@uni-siegen.de, bvasilyev_aa@mail.ru, cpeter.wilbrandt@physik.uni-goettingen.de, dkaffenberger@lot.mb.uni-siegen.de

Keywords: Case hardening engineering, alloy dependent carbon diffusivity, modeling of diffusion coefficients, gas carburizing experiments, secondary ion mass spectroscopy.

ABSTRACT

The simulation of carburization profiles for online computer aided control of carburizing processes

and offline case hardening engineering represent the most important technical application of

carbon diffusion coefficients in austenite. The question of whether substitutional alloying elements

such as Cr, Mn, Mo, Ni or Si, at low concentrations around 1 wt.% typical of the used steels

influence the diffusivity considerably, is increasingly raised recently. In the present paper, the

materials science tool SimCarb Diffusivity is introduced as one module of the stand-alone SimCarb

program package for the numerical simulation of case hardening. The Windows expert software

suite comprises the process steps of carburizing, quenching and tempering. The new program

SimCarb Diffusivity calculates alloy dependent diffusion coefficients of carbon in the austenite of,

e.g., case hardening steels for SimCarb simulations. The implemented physically based model is

described in detail. A numerical process study indicates a crucial alloy related effect of the

diffusivity on the resulting carburization and hardness profiles, even within the specification of

individual steel grades. Carburizing experiments on 18NiCrMo14-6 and 15NiCr13 are evaluated.

The carbon distributions are measured by secondary ion mass spectroscopy. Strong evidence is

found that the diffusion coefficient of carbon in austenite depends significantly on the steel

composition, which should be taken into account in the process control of carburizing.

Introduction

Case hardening is the most commonly applied heat treatment of steel. The high hardness, fatigue

strength and wear resistance of the carbon enriched edge material is combined with the good shock

load capacity from the ductile tough core microstructure. Journals, bolts, screws, shafts, gears,

bearings, cams, crown wheels, spindles, levers and tools, for instance, are frequently case hardened.

The service performance of the produced parts and the economic efficiency of the heat treatment

depend on a high process quality. As main target parameter, the (case) carburization depth, CCD,

taken at a carbon concentration 0.35 wt.%, should not deviate more than ±0.1 mm from the desired

value [1]. In current practice, however, a discrepancy of ±0.25 mm already represents a good heat

treatment result [2], which is often actually not achieved [3]. The sufficiently precise predictability

of the carburization profile requires an adequate knowledge of the diffusion coefficient of carbon in

the austenite phase of the used steel. Alloy dependent expressions are provided by the new software

SimCarb Diffusivity introduced in the present paper [4]. Accurately simulated carbon distributions

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are the prerequisite for the correct prediction of the resulting hardness profiles. The corresponding

main process target parameter is the case hardening depth, CHD, taken at 550 HV or 52.5 HRC.

Fundamentals of Case Hardening of Steel

The first step of case hardening is the thermochemical process of diffusion of carbon usually from

controllable gas mixtures of thermally decomposed hydrocarbons into the near-surface zone of the

steel at temperatures between 800 and 1100 °C, mostly from 850 to 980 °C. Fast oxygen-containing

atmospheres reach high mass transfer coefficients β of 1×10−5

to 4×10−5

cm/s. Today's industry

standard is two-step boost-diffuse normal pressure gas carburizing. Fig. 1 illustrates this process. In

the boost stage, the carbon potential, cp, is adjusted as high as possible below the carbide and soot

limit, usually between 0.8 and 1.3 wt.% C, to achieve maximum enrichment depth at minimum

annealing time. The subsequent diffuse period equalizes the steep concentration gradient in the edge

zone. The carbon potential is reduced to 0.55 to 0.95 wt.% C. The diffuse treatment takes about a

quarter of the boost time. The final surface concentration usually amounts to 0.6 to 0.85 wt.% C.

The basic carbon content, c0, of steels for case hardening ranges from 0.07 to 0.30 wt.%.

Fig. 1.

Carbon concentration profiles after the

boost and subsequent diffuse stage of a two-

step carburizing process and evaluation of

the respective case carburization depth,

CCD.

The carburized parts are hardened by quenching with suitable cooling rate, e.g. in oil or water.

Direct, single and double hardening can be applied. Tempering commonly occurs between 150 and

200 °C for 2 to 4 h. Typical hardness profiles are shown in Fig. 2. The desired carburization and

case hardening depths vary from 0.05 to 10 mm, frequently from 0.5 to 4 mm. Target values of the

surface hardness are 58 to 65 HRC, whereas 30 to 50 HRC in the core are generally appropriate.

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Fig. 2. Hardness distributions after quenching and tempering of the carburized workpiece of Fig. 1

and evaluation of the respective case hardening depth, CHD.

Case Hardening Engineering

To realize the indicated potential for optimization, a systematic procedure of process analysis and

simulation is required. Case hardening engineering involves the accurate computational prediction,

experimentally verified calibration and parameter study based strategy development of the heat

treatment. Continuous monitoring finally ensures the stability of optimized processes.

Process analysis

The case hardening parameters must be known. The time dependent properties of the endothermic

carburizing gas, i.e. the carbon potential and the mass transfer coefficient, are measured by the foil

method. An example is shown later in Fig. 19. The heat transfer coefficient during quenching is

deduced from cooling curves. The alloy factor, carbon diffusivity and hardenability are governed by

the steel composition. It is determined by chemical check analysis. The influence of the alloying

elements on the carbon diffusion coefficient (CDC) in austenite is examined in detail in the follow-

ing. The hardenability of the steel can be considered, e.g., by time-temperature-transformation

diagrams for continuous cooling or Jominy curves. The carburization and hardness profiles have to

be measured with high accuracy to allow reliable nominal/actual value comparisons. The quenching

microstructure can be characterized with reasonable significance by the retained austenite content.

Part of case hardening engineering is, moreover, the development and application of appropriate

experimental methods. The alloy factor of a certain steel, for instance, can be derived from through

carburized foils by comparing the total carbon content with reference samples of binary Fe–C. The

microstructure should be adjusted representatively. Isothermal powder pack decarburization is

suitable for determining the concentration dependent diffusion coefficient of carbon in austenite of

the non- or low-alloyed steels. Completely through carburized samples or model materials, such as

Fe–1 wt.% Cr–1 wt.% C, can be used. Hollomon-Jaffe parameters or carbon dependent diminution

factors of martensite evaluated experimentally allow the prediction of the tempering hardness.

Process simulation

Computer aided case hardening engineering is based on an efficient simulation tool. The SimCarb

program suite used in the present paper is outlined in the section after next. Expert software allows

the simulation of complex carburizing case hardening processes. Systematic nominal/actual value

comparisons form the basis for status analyses. The approach is particularly illustrated by carbon

depth profiles later in Figs. 21, 23, 27 and 28. Also, fundamental process understanding becomes

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available, e.g. by computational parameter studies, that is not or only with much effort accessible in

the experiment.

Alloy Dependence of the Diffusion Coefficient of Carbon in Austenite

The prediction of carburization depths for case hardening represents the most important practical

application of the diffusivity of carbon in austenite [5]. Experimental data on multi-component iron

based systems are rare. The general statement in the literature, however, is that the small amounts of

maximal few wt.% of alloying elements, such as Cr or Si, typical of steels for case hardening do not

significantly influence the carbon diffusion coefficient [6–8]. Expressions on binary Fe–C austenite

are therefore used in state-of-the-art furnace control software for online simulations of carburization

profiles [9]. On the other side, a noticeable dependence of the carbon diffusivity in austenite on the

alloy composition is indicated, for instance, by decarburization experiments [10–13]. These findings

are supported by recent metal physics based analyses [4, 14, 15]. The question addressed in this

paper of how alloying elements in case hardening steels change the diffusion coefficient and profile

of carbon as well as particularly the carburization depth is of essential importance for achieving the

required high process reliability.

SimCarb Diffusivity in the Sequence of the Stand-Alone SimCarb Software Suite

Advanced expert simulation tools provide a deeper insight into heat treatment processes of steels

and disclose optimization potentials in terms of quality and productivity, energy as well as resource

efficiency. The commercial SimCarb software suite for offline computer aided case hardening engi-

neering in industry and research consists of three interacting Windows programs. The carburizing

module SimCarb, introduced at the MMT-2006 conference [16], allows the numerical simulation of

carbon profiles for complex user-defined multi-stage or continuously controlled isothermal and

temperature programmed carburization processes by the finite difference method. The software also

includes a basic hardness calculation. In addition to the relevant SimCarb library of 18 entries, the

extension module SimCarb Diffusivity, introduced in the present paper, provides steel specific

carbon diffusion coefficients in austenite containing substitutional alloying elements (SAE). The

expressions of exponential concentration dependence, which are derived from the quantitative

physically based model described in the next section [4], serve as optional manual SimCarb input.

The simulation of case hardening is completed by the advanced microstructure prediction and

hardness computation of SimCarb QuenchTemp. Carbon depth distributions calculated by SimCarb

are directly importable but arbitrary carburization profiles can be programmed in an implemented

subroutine and entered by the user as well. The quenching process is simulated by the finite

difference method, based on continuous cooling (time-temperature-) transformation diagrams. The

tempering hardness is deduced from experimentally determined Hollomon-Jaffe parameters.

SimCarb QuenchTemp is also introduced at the MMT-2012 conference in a separate paper.

SimCarb Diffusivity Model of Alloy Dependent Carbon Diffusion Coefficients in Austenite

The model for calculating the carbon diffusion coefficient, DC, in complexly alloyed austenite [4],

implemented in the SimCarb Diffusivity software, is based on a microscopic analysis of the

diffusion of carbon atoms. The approximation of average energies is used [17]. The following

formula holds for the CDC in Fe–C–Xi austenite [4], where Xi is a substitutional alloying element:

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T

yyyyUyyyyU

T

TLyTLyyyDyyTD

iiiiiiii

i

i

i

i

i

R

γα)(exp

R

γα)(θexp

R

)()(1121),,(

XXCXXCCXXCXXCC

XVaC,X

XVaC,X

CC0C,XCC

(1)

The concentrations of the carbon and Xi atoms are respectively expressed as site fractions yC and

iyX . Furthermore, ΔUC(yC)=ΔUC,0+αCyC represents an effective activation energy for the migration

of carbon atoms in Fe–C austenite, iLX

Va,C(T) is a thermodynamic parameter and DC,0, ΔUC,0, αC, θ,

iXα as well as iXγ are empirical quantities. The model parameters DC,0, ΔUC,0, αC and θ are deter-

mined, utilizing the experimental CDC data for the Fe–C alloys [6, 18], on the basis of minimization

of the average relative deviation, δ , of the theoretical results from the experimental data:

exp

D

1exp

,C

exp,C,C

thC

expD

),(1δ

N

j j

jjj

D

DyTD

N (2)

Here, expDN is the number of elements in the database, Tj and yC,j are respectively the temperature

and carbon concentration corresponding to the j-th experimental CDC value. The experimental and

theoretical carbon diffusion coefficients obtained using the formula of Eq. (1) are accordingly

denoted exp,C jD and ),( ,C

thC jj yTD .

The core of the experimental database is represented by the data on CDC investigations carried

out for temperatures between 800 and 1305 °C in the range of carbon concentration from 0.22 to

1.35 wt.%, i.e. 0.01<yC<0.06 [6]. The most reliable and self-consistent CDC values are selected.

Some data, which are obviously out of the general trend of the diffusivity change with temperature,

and also the data, obtained for samples with rather high SAE (Mn, Si and, especially, Cr) content,

are excluded from consideration. The performed selection of the diffusion data minimizes the effect

of distortion of the values of the model parameters for binary Fe–C alloys caused by experimental

errors and the systematic SAE influence on carbon diffusivity.

The CDC data from Ref. [6] are enlarged by including the additional data obtained in Ref. [18]

for the temperatures of 1000, 1100 and 1200 °C and the carbon concentrations of 0.2, 0.4 and 0.7

wt.%, i.e. site fractions yC of about 0.009, 0.019 and 0.033. As a result, a database consisting of 75

experimental CDC values is utilized at the first stage of the model calibration.

Table 1 lists the optimal model parameters values and the corresponding data used in the well-

known model of Ågren [5]. As may be seen from this comparison, the derived values of the carbon

diffusion activation energy in γ iron, ΔUC,0, and of the parameter αC, determining the rate of

activation energy decrease with increase of the carbon concentration, are considerably lower than

suggested by Ågren. According to Fig. 3, all CDC values from Ref. [6] agree well with the calcula-

tions, as well as the data from [18]. The later is rather important in view of the further use of the

CDC investigation results in the Fe–C–Хi alloys [19–22], which are deduced with the application of

the same experimental approach.

Table 1. Two sets of the model parameters for binary Fe–C austenite.

Model DC,0×107 in m

2/s ΔUC,0 in J/mol αC in J/mol θ×10

4

SimCarb Diffusivity [4] 1.41 137772.1 −231000 2.588

Ågren [5] 4.53 147714.8 −219789 2.221

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On the basis of the obtained results, one may conclude that the SimCarb Diffusivity model

predicts CDC of Fe–C alloys throughout the whole studied interval of temperatures and carbon

concentrations much more precisely than the Ågren model [5], as well as recently suggested

alternative approaches [14, 15].

Fig. 3.

Correlation between the measured and calculated

CDC values for the binary Fe–C alloys. The

accuracy bars correspond to ±7% from the meas-

ured value [6, 18].

At the second stage of model calibration, the values of the parameters iXα and

iXγ , describing

the effect on CDC caused by an additional alloying with the substitutional element Xi, are evaluated

according to Table 2. These parameters are determined, utilizing experimental investigation data on

the carbon diffusivity in the ternary alloys Fe–C–Xi (Xi=Cr, Mn, Mo, Ni, Si, Al, W, Co) with differ-

ent carbon and SAE contents [19–22]. The appropriate data are available for the three temperatures

of 1000, 1100 and 1200 °C and carbon concentrations of 0.2, 0.4 and 0.7 wt.%. Data for alloys

containing SAE in the following amounts are utilized: 1.0 and 2.5 wt.% Cr; 1.0 and 12.0 wt.% Mn;

0.9 and 1.55 wt.% Mo; 4.0 and 9.5 wt.% Ni; 1.6 and 2.55 wt.% Si; 0.7, 1.7 and 2.45 wt.% Al; 0.5,

1.05 and 1.95 wt.% W; 6.0 and 11.0 wt.% Co.

Table 2. Some values of the model parameters, determining the effect of substitutional alloying

elements on the diffusion coefficient of carbon in Fe–C–Хi austenite.

Element Xi iXα , J/mol iXγ , J/mol

Si ↓ 192650 –4044000

W 2080000 –27820000

Cr (< 2.5 wt.%) 1041871 –2117897

Co –41340 –1702000

According to Fig. 4, the results of the CDC calculation agree rather well with the experiment for

all ternary alloys. The obtained accuracy of the experimental data reproducing is much higher than

for the alternative models calibrated using the same experimental data set [14, 15, 23]. Such a

difference is related to the fact that the major (exponential) effect of the CDC change in the ternary

alloys is caused by a variation of the diffusion frequency factor due to the corresponding change in

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the effective energy of the carbon diffusion that is more precisely taken into account in the SimCarb

Diffusivity model.

The developed model allows to describe a slowing down of the diffusion of carbon owing to the

carbide forming elements (Cr, Mn, Mo, Al, W), which reduce its activity in an austenite, as well as

diffusion acceleration by elements (Ni, Co) raising the carbon activity. The main part of the effect

of the SAE is caused by their influence on the effective activation energy of diffusion. Additional

alloying by Cr, Mn, Mo, Al and W results in an increase of the activation energy (iXα > 0), whereas

additions of Ni and Co reduces this energy (iXα < 0).

A synergetic effect on the diffusion activation energy, which occurs when the austenite simulta-

neously contains the carbon and substitutional alloying elements Xi, is described by means of the

iXγ parameter. A negative value of iXγ for the carbide formers (see Table 2) corresponds to a de-

crease of the activation energy with an increase of carbon content or, accordingly, to a depression of

their slowing down effect. This parameter is also negative for such elements as Ni and Co (cf. Table

2). Therefore, the synergetic effect is the opposite for them.

Assuming the additivity of SAE effects on the thermodynamic factor and the effective activation

energy of the carbon diffusion, the formula of Eq. (1) for CDC in ternary Fe–C–Хi alloys may be

generalized for the case of a multi-component alloy Fe–C–Х1–…–XN, containing a number (X1, …,

XN) of SAE. In this case the carbon diffusion coefficient may be calculated as follows:

T

yyyyUyyyyU

T

TLyTLy

yyDyyyTD

N

i

N

i

N

i

N

i

N

i

N

i

iiiiiiii

i

i

i

i

N

R

γα)(

expR

γα)(

θexp

R

)()(1

121),...,,,(

1XXC

1XXCC

1XXC

1XXCC

XVaC,

1X

XVaC,

1X

CC0C,XXCC 1

(3)

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Fig. 4. Correlation between measured and calculated CDC values for the ternary Fe–C–Xi alloys

(Xi=Cr, Mo, Ni, Si). The accuracy bars correspond to ±7% from the measured value.

According to experimental data [22], the effect of Si on the carbon diffusivity depends on the

temperature. Above 1000 °С, Si decreases the diffusivity of carbon (Si ↓, see Fig. 4), whereas below

this temperature, on the contrary, it significantly increases the diffusion coefficient (Si ↑). The set of

values of model parameters determining the effect of Si on CDC at low temperatures is evaluated,

according to Fig. 5a, on the basis of data given in Ref. [24] by processing experimental carbon

profiles of carburizing AISI 8620 steel containing 0.19 wt.% Si, 0.87 wt.% Mn, 0.57 wt.% Cr, 0.21

wt.% Mo and 0.42 wt.% Ni. As per the obtained results, Si should have an intense accelerating

effect on carbon diffusion at low temperatures Т<1000°С: iXα = –777300,

iXγ = 700200 J/mol. As

illustrated in Fig. 5b, the version of the model used in SimCarb Diffusivity with an accelerating

influence of Si predicts a significant increase in CDC by alloying with 0.2 to 0.3 wt.% of this

element, which represents a typical content of Si in many case hardening and other practically

important steels (see also next section, Fig. 8).

It is worth noting that the CDC calculations, according to the SimCarb Diffusivity model [4],

correlate with existing experimental data substantially better than predictions of the alternative

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models [5, 14, 15, 23]. Therefore, it is recommended for the simulation of carburization profiles in

case hardening (see below) and several other technological applications, such as decarburization.

Fig. 5. Calculated CDC dependences on carbon concentration for alloys of different compositions.

a) AISI 8620 steel; b) illustration of the acceleration effect (Si ↑) of Si alloying.

Influence of Main Steel Alloying Elements on the Carbon Diffusion Coefficients of Austenite

According to the model outlined above, substitutional alloying elements significantly change the

bulk diffusion coefficient of carbon in austenite, DC, even at the usual low concentrations around 1

wt.% of steels for case hardening. The predictions of SimCarb Diffusivity at different temperatures

are further discussed in this section by means of Figs. 6 to 8. The carbon concentration cC of 0 to

1.4 wt.% covers the common range of carburizing. The relevant additions of the main alloying

elements Cr, Mn, Mo, Ni and Si are considered.

Fig. 6.

SimCarb Diffusivity prediction of the effect

of typical Cr, Ni and Mo contents (in wt.%)

on the concentration dependent diffusion

coefficient of carbon in ternary austenite at

900 °C.

Carbide formers tend to reduce the carbon diffusivity. This is evident for Mo up to 1.1 wt.% C

and particularly for Cr at 900 and 970 °C from Figs. 6 and 7, respectively. Both tendencies agree

with literature statements [10, 12, 13, 25]. The predicted influence of Cr is higher than indicated in

previous sources [10, 25]. An alloy addition of 0.5, 1, 1.5 and 2 wt.% Cr at 900 °C (cf. Fig. 6), for

instance, approximately corresponds to the carbon diffusion coefficient in binary Fe–C austenite at

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868, 837, 810 and 782 °C, respectively. The effect of 0.5 to 2 wt.% Cr is thus comparable with a

temperature reduction of about 30 to 120 °C. The influence of Mn can practically be neglected. In

the relevant ranges of temperature from 850 to 1100 °C and carbon concentration between 0 and 1.4

wt.%, technically common contents of up to 2 wt.% Mn result in a DC change of only around ±3%.

Fig. 7.

SimCarb Diffusivity prediction of the effect

of typical Cr, Ni and Mo contents (in wt.%)

on the concentration dependent diffusion

coefficient of carbon in ternary austenite at

970 °C.

The austenite stabilizers Ni and Si are also analyzed. The tendency of these alloying elements is

to increase the carbon diffusivity. Ni shows the expected effect, reported in the literature [12], in the

whole temperature range relevant to carburizing, as indicated at 900 and 970 °C respectively in

Figs. 6 and 7. The predicted influence of Si is more complex, as discussed in the previous section.

At most common carburizing temperatures up to 980 °C, Si greatly increases the diffusion coeffi-

cient of carbon in austenite. According to Fig. 8, a rise of over 20% and about 50% corresponds to

usual alloying additions of 0.2 and 0.4 wt.% Si, respectively, at 900 and 970 °C. A transition occurs

around 1000 °C. At higher temperatures, a slight reversed effect on the carbon diffusivity, e.g. at

1050 °C in Fig. 8, of −5% to +1% at 0.2 wt.% Si and −9% to +2% at 0.4 wt.% Si, again compared

with binary Fe–C austenite, is obtained. This prediction should attract more interest in the future.

Fig. 8. SimCarb Diffusivity prediction of the temperature dependent Si effect (typical alloying con-

tents) on the carbon diffusion coefficient in Fe–C–Si austenite at 900, 970 and 1050 °C.

Carbon Diffusivity in Case Hardening Steels in Relation to the Result of Carburizing

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By entering the chemical composition into the input mask and pressing the button to start the

calculation, SimCarb Diffusivity provides the SimCarb user with the predicted concentration

dependent expression of the diffusion coefficient of carbon in the austenite of the alloy concerned.

Fig. 9 demonstrates on the example of six conventional case hardening steel grades at the typical

carburizing temperature of 900 °C that large differences occur.

Fig. 9. SimCarb Diffusivity prediction of the concentration dependent diffusion coefficient of car-

bon in the austenite of the indicated steels at 900 °C and binary Fe–C for comparison.

The mean chemical compositions of the materials are considered according to the standard speci-

fication. The concentrations i

cX of the main substitutional alloying elements Xi, the individual

effects of which on the carbon diffusivity arise from Fig. 6, are summarized in Table 3. Compared

in Fig. 9 with binary Fe–C austenite, the diffusion coefficient DC is increased by up to about 65%

(±12%) in X12Ni5 and reduced by up to 69% (±6%) in 18CrNi8. Einstein’s equation, ‹x›2=2Dt,

suggests a square root dependence of the carburization depth upon the carbon diffusivity. Here, ‹x›

and t respectively denote the average diffusion distance and the time.

Table 3. Designation, material number and chemical composition of the steels of Fig. 9. The case

carburization depths calculated for the gas carburizing process of Fig. 10 are also given.

Steel Designation Mat. No. Cr, wt.% Mn, wt.% Mo, wt.% Ni, wt.% Si, wt.% CCD, mm

18CrNi8 1.5920 1.95 0.50 0 1.95 0.275 2.00

18NiCrMo14-6 1.3533 1.45 0.55 0.20 3.50 0.20 2.38

17Cr3 1.7016 0.85 0.75 0 0 0.20 2.87

15NiCr13 1.5752 0.75 0.55 0 3.25 0.20 3.11

Fe–C (reference) --- 0 0 0 0 0 3.54

C15 1.0401 0 0.45 0 0 0.20 3.92

X12Ni5 1.5680 0 0.55 0 5.00 0.35 4.53

Carburizing of different steel grades

To estimate the impact on the resulting carbon depth profiles, the conventional isothermal 80 h two-

step boost-diffuse gas carburizing process of Fig. 10 is analyzed. At the assumed temperature of

900 °C, the predicted diffusivities of Fig. 9 can be applied. The carbon potential of 1.0 wt.% in the

boost or saturation stage of 64.5 h is lowered linearly during 30 min to 0.6 wt.% in the diffuse step.

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The mass transfer coefficient simultaneously rises from 2.0×10−5

to 2.5×10−5

cm/s. A decrease of

the carburizing speed β in the equalization phase due to the changing gas composition is unlikely.

The SimCarb simulations of this typical process are displayed in Fig. 11. To achieve direct com-

parability, a uniform initial carbon content, c0=0.17 wt.%, is assumed for the binary Fe–C reference

material as well as for all steels in Table 3. Only for X12Ni5, this value slightly exceeds the specifi-

cation limits by 0.02 wt.% C. A unity alloy factor, ka=1, simply accounts for the actual adjustment

of the carbon potential to the target parameters (surface C concentration), material and process con-

ditions [16].

Fig. 10.

Process conditions for a numerical

analysis of gas carburizing of the

steels of Fig. 9. The carbon

potential and the mass transfer

coefficient are plotted as a function

of time.

The differing diffusion coefficients of Fig. 9 result in strongly deviating concentration profiles.

From Fig. 11, the main process target value of the carburization depth, CCD, is indicated for all

simulated case hardening steels in the diagram and correspondingly included in Table 3. Compared

with CCD=3.54 mm for binary Fe–C austenite, an increase by 0.99 mm (28%) and a reduction by

1.54 mm (44%) is found in X12Ni5 and 18CrNi8, respectively. The alloy dependent carburization

depths vary widely from 2.00 to 4.53 mm (cf. Fig. 11). This finding clarifies the recommendations

in the literature that the chemical composition of the steel must be considered in the calculation of

the carbon diffusivity in austenite and that certain grades should be carburized separately [24, 26].

Fig. 11.

Carburization profiles for the process of Fig.

10 simulated for the steels of Fig. 9 of initial

carbon content c0=0.17 wt.%. A uniform

alloy factor of ka=1 allows direct comparabil-

ity.

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Admissible variations in the chemical composition of individual steel grades

The alloy composition can differ from batch to batch within the specification limits of the standard.

It is therefore of particular interest to assess the effect of such admissible variations on the diffusion

coefficient of carbon in austenite and resulting carburization profiles. The two-step process of Fig.

10 is studied. The case hardening steel grades 17Cr3 and 18NiCrMo14-6 are chosen for the simula-

tion. The contents of the substitutional alloying elements corresponding to the minimum (min.),

average (avg.) and maximum (max.) carbon diffusivity DC are given in Table 4.

Fig. 12 reveals the predicted diffusion coefficients. The maximum and minimum values deviate

upwards and downwards from the average alloy compositions, also shown in the diagram, respec-

tively by about 40% and 30%.

Table 4. Steel composition of minimum, average and maximum carbon diffusivity for 17Cr3 and

18NiCrMo14-6. The carburization and case hardening depths refer to actual alloy factors.

Designation DC Cr, wt.% Mn, wt.% Mo, wt.% Ni, wt.% Si, wt.% CCD, mm CHD, mm

18NiCrMo14-6

min. 1.60 0.70 0.25 3.25 0 2.18 2.43

avg. 1.45 0.55 0.20 3.50 0.20 2.48 2.75

max. 1.30 0.40 0.15 3.75 0.40 2.81 3.15

17Cr3

min. 1.00 0.90 0 0 0 2.70 2.68

avg. 0.85 0.75 0 0 0.20 3.06 3.05

max. 0.70 0.60 0 0 0.40 3.46 3.42

Fig. 12.

SimCarb Diffusivity prediction of alloy de-

pendent carbon diffusion coefficients of

17Cr3 and 18NiCrMo14-6 steel for

average composition and at specification

limits according to Table 4.

The carburization profiles for the two-step process of Fig. 10 are plotted in Fig. 13. In this simu-

lation, the actual alloy factors ka are used in accordance with the default of the SimCarb library [9].

The values also vary within the different steel compositions from 1.036 (at max. DC) to 1.132 (min.

DC) for 17Cr3 and from 0.997 (max. DC) to

1.106 (min. DC) for 18NiCrMo14-6, which

is reflected in the surface carbon content

[16]. Again, the initial concentration c0 is

set at 0.17 wt.% C.

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Fig. 13.

Carburization profiles for the process of Fig. 10 simulated for the steels of Fig. 12 by considering

the SAE dependent actual alloy factor ka [9].

The carburization depths, evaluated in Fig. 13, are added in Table 4. The large deviations from

the mean alloy composition (avg. DC) to higher and lower values by more than 10% amount to

+0.40 and −0.26 mm for 17Cr3 as well as +0.33 and −0.30 mm for 18NiCrMo14-6, respectively.

The differing carburization profiles also lead to deviating hardness distributions. Quenching of

long cylinders of 50 mm diameter in mildly agitated oil is exemplarily simulated by means of

SimCarb QuenchTemp. Single hardening of 17Cr3 and 18NiCrMo14-6 steel from an austenitizing

temperature of 870 and 830 °C, respectively, is considered. The ASTM austenite grain size number

[27], KASTM, is assumed to be 8. The microstructural composition is calculated as well.

Fig. 14 reveals the simulation results for the three variants of 17Cr3 steel of Fig. 13. Note that

the carburization and case hardening depths indicated in Table 4 are almost identical. The volume

fractions vi of the occurring phases i are also plotted in Fig. 14 as a function of depth. The steel con-

tains quite small additions of alloying elements so that only little retained austenite of maximum 3%

(min. DC) remains in the edge zone of carbon content around 0.65 wt.%. The limited hardenability

of 17Cr3 is manifested in the drop of the martensite amount in the region of falling carbon con-

centration. Bainite and particularly ferrite is formed increasingly with distance from surface. The

martensite content at the case hardening depth is slightly above 70% for each variant.

Fig. 14.

SimCarb QuenchTemp simulation of

quenching results of 17Cr3 steel

according to Fig. 13. The depth de-

pendent hardness (CHD indicated)

and microstructural composition are

displayed. The phase contents vi of

martensite, retained austenite, bainite

and ferrite are predicted.

The SimCarb QuenchTemp simulation results for the three 18NiCrMo14-6 steel compositions

are shown in Fig. 15. The predicted retained austenite content in the edge zone of this much better

hardenable grade, enriched to about 0.65 wt.% C, equals up to 11%. The amount of martensite

remains above 90% even far below the depth where the core hardness is reached. No ferrite occurs.

The case hardening exceeds the carburization depth by more than 10%, as evident from Table 4.

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Fig. 15. Simulated quenching results of 18NiCrMo14-6 steel according to Fig. 13 (cf. Fig. 14).

Experimental Procedure

Carburizing experiments are evaluated to verify and analyze the predictions of the physically based

model of calculating alloy dependent diffusion coefficients of carbon in austenite implemented in

the new SimCarb Diffusivity software. The applied processes, steels and testing methods are briefly

described in the following.

Carburizing case hardening experiments

Isothermal boost-diffuse gas carburizing treatments are performed at the same temperature of 970

°C. Diameter and height of the cylindrical samples are large compared to the depth of the diffusion

zone. Two case hardening steel grades are chosen, for which the predicted carbon diffusivity differs

significantly (cf. Fig. 9). For 18NiCrMo14-6, a shorter (about 13 h) and a longer (44 h) carburizing

time are applied in experiment N° 1 and N° 2, respectively. Different chemical compositions within

the specification limits are used. For 15NiCr13, a carburizing period of about 50 h, i.e. quite similar

to the longer process time for 18NiCrMo14-6, is chosen in experiment N° 3. The carbon potential in

the boost and diffuse step is consistently set at 1.20 and 0.79 wt.%, respectively.

Quenching in still oil and tempering at 200 °C for 2 h are exemplarily examined in experiment

N° 2. The hardness depth profiles and the composition of the microstructure are measured and

simulated. The diameter and height of the sample cylinders respectively amount to 90 and 90 mm,

40 and 80 mm as well as 30 and 100 mm in experiment N° 1, N° 2 and N° 3.

Experimental and measurement methods

Carbon potential and mass transfer coefficient in the boost and diffuse process step of carburizing

are determined by means of the foil method using thin pure Fe–C sheets (c0=0.1 wt.% C) of 100 µm

thickness. The evaluation is demonstrated below for experiment N° 1 in Fig. 19. The concentration

of the substitutional alloying elements of the used case hardening steels is analyzed by optical

emission spectrometry. The carbon content, e.g. of the foils or in the core of the cylindrical samples

(initial value c0), is measured by applying the dry combustion method.

The carburization profiles are determined by secondary ion mass spectroscopy (SIMS) on pol-

ished sections of central disks [13]. The required data calibration is performed by means of a carbon

reference value in the core or, as c0 is rather low, in the enriched near-surface zone.

The distributions of the micro hardness are measured by Vickers indentation technique. The

HV1 values are converted to HRC. The retained austenite content is deduced from an X-ray dif-

fraction (XRD) phase analysis applying Mo Kα radiation. The XRD line broadening correlates with

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the hardness. It is determined as full width at half maximum (FWHM) intensity by applying Cr Kα

radiation.

Reference Data of the Diffusion Coefficient of Carbon in Binary Fe–C Austenite

For analyzing the carburization profiles produced in the case hardening test steels in terms of

literature data of the carbon diffusivity in binary Fe–C austenite by comparative simulations, the

widely accepted work of Tibbetts is used [8]. This reference, which represents the default of the

corresponding SimCarb library [16], agrees well with earlier findings [6]. The investigated

concentration and temperatures range from 0.2 to 1.3 wt.% C and 975 to 1075 °C, respectively, is

suitable for the applied conditions of the carburizing processes (see above).

The data of Tibbetts, deduced from steady-state diffusion coefficient measurements, are chosen

because they are based on AISI 1010 steel (similar to St34-2, Mat. No. 1.0032). The predictions of

SimCarb Diffusivity for this low Mn alloyed grade and binary Fe–C austenite do not differ signifi-

cantly from each other. The chemical composition of AISI 1010 test steel is not reported in Ref. [8].

The customary additions of substitutional alloying elements are given in Table 5. The graphical

representation of the concentration dependence of the diffusion coefficient of carbon in austenite at

the experimental carburizing temperature of 970 °C in Fig. 16 refers to the mean steel composition

(0.45 wt.% Mn). The diagram reveals that the deviations remain small even if a Si content of 0.05

wt.% is considered.

Table 5. Designation, UNS (unified numbering system) number and specified chemical composi-

tion of AISI 1010 and AISI 4130 steel considered in the analysis of the carbon diffusivity.

Steel Designation UNS No. C, wt.% Cr, wt.% Mn, wt.% Mo, wt.% Ni, wt.% Si, wt.%

AISI 1010 G10100 0.08–0.13 0 0.30–0.60 0 0 ≤ 0.10

AISI 4130 G41300 0.28–0.33 0.80–1.10 0.40–0.60 0.15–0.25 ≤ 0.25 0.15–0.35

Fig. 16. Carbon diffusion coefficient of Tibbetts and predicted by SimCarb Diffusivity for binary

Fe–C and AISI 1010 steel without (coincident with Fe–C) and with Si addition at 970 °C.

It is worth mentioning that Tibbetts includes diffusivity data of AISI 4130 steel (similar grade to

34CrMo4, Mat. No. 1.7220) at 1075 °C in the evaluation. Thus, Fig. 17 shows the correspondingly

extended comparison of Fig. 16 at this temperature, where again the mean chemical composition

according to Table 5 is used for modeling. Above 1000 °C, the Si effect is small (cf. Fig. 8) and can

be neglected for AISI 1010 steel. Note that the data of Tibbetts for the carbon diffusion coefficient

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agree well with the predictions of SimCarb Diffusivity for binary Fe–C austenite in the whole

temperature range from 975 to 1075 °C. This is another reason of the choice made for the following

reference simulations of the carburization profiles.

Fig. 17. Diffusion coefficient of carbon in austenite at 1075 °C according to the data of Tibbetts

and predicted by SimCarb Diffusivity for binary Fe–C, AISI 1010 and AISI 4130 steel.

In Fig. 17, however, SimCarb Diffusivity predicts a considerably lower carbon diffusion coeffi-

cient in austenite of AISI 4130 steel at 1075 °C. Small Ni additions within the specification of up to

0.25 wt.% do not change the dot-and-dashed line in the diagram significantly. Note that AISI 4130

steel contains a rather high amount of Cr, for which indication of a decreasing effect on the carbon

diffusivity in austenite from the literature is discussed above.

Chemical Composition of the Test Steels and Predicted Carbon Diffusivity in Austenite

The determined basic carbon content, c0, and the concentrations of the substitutional alloying

elements are summarized in Table 6 for the test steels used in the carburizing case hardening

experiments N° 1 to N° 3. The typical measuring accuracy is better than 5%.

The predictions of the concentration dependent diffusion coefficient of carbon in the austenite of

these test steels and of binary Fe–C at 970 °C by SimCarb Diffusivity are compared with the data of

Tibbetts in Fig. 18. The differing DC values of the two alloys of 18NiCrMo14-6 are clearly lowest.

A less deep carburization profile as expected from the reference data of Tibbetts is thus suggested.

Due to the effect of Ni and Si at reduced Cr content, the carbon diffusivity in 15NiCr13 is not much

smaller than in binary Fe–C austenite, according to the predictions of SimCarb Diffusivity. The data

of Tibbetts lie in the same range.

Table 6. Designation, experiment number and measured chemical composition of the test steels.

Steel Designation Experiment C, wt.% Cr, wt.% Mn, wt.% Mo, wt.% Ni, wt.% Si, wt.%

18NiCrMo14-6 N° 1 0.165 1.533 0.599 0.166 3.229 0.228

18NiCrMo14-6 N° 2 0.180 1.366 0.446 0.180 3.318 0.261

15NiCr13 N° 3 0.150 0.690 0.418 0.069 3.323 0.291

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Fig. 18.

Carbon diffusion coefficient of Tibbetts and

predicted by SimCarb Diffusivity for the test

steels 18NiCrMo14-6 (two compositions) and

15NiCr13 as well as binary Fe–C at 970 °C.

Carburizing Experiment N° 1 on 18NiCrMo14-6

Carbon potential cp and mass transfer coefficient β are determined during the carburizing process by

means of the foil method. An example of the evaluation of the measurements is shown in Fig. 19 for

the boost stage of experiment N° 1. As for the thin Fe–C sheets of thickness d=100 µm, βd<<DC is

valid, the carbon content of the foil, foilCc , increases with exposure time, t, from the initial concen-

tration, foil0c , toward the cp saturation level according to the following relationship [9]:

t

dccctc

β2exp)( foil

0ppfoilC (4)

Fig. 19. Total carbon content of six foils as a function of the respective carburizing time.

The mass transfer coefficient β is obtained as fitting parameter from Eq. (4). The complete two-

step scheme of the carburizing process of experiment N° 1 is illustrated in Fig. 20. The carbon

potential and the mass transfer coefficient in the boost and diffuse stage are respectively denoted by bpc and βb as well as

dpc and βd. The applied values are given in the diagram.

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Fig. 20. Conditions of isothermal two-step boost-diffuse gas carburizing of experiment N° 1 re-

vealing the development of the carbon potential and mass transfer coefficient with time.

Fig. 21 shows the SIMS data points of the spatially resolved determination of the carbon con-

centration. The results of two independent measurements performed at different locations of the

sectioned cylindrical disk fall within the respectively indicated scatter bars of 10% of the absolute

value, which illustrates the accuracy of the method. The carburization depth, CCD, is taken from

the SIMS data to be about 1.7 mm. The carbon profiles simulated by SimCarb for the predictions of

SimCarb Diffusivity for binary Fe–C and the specific 18NiCrMo14-6 steel composition (N° 1 in

Table 6) as well as for the DC reference of Tibbetts are also plotted in Fig. 21. The alloy factor, ka,

according to the AWT recommendation (Association for Heat Treatment and Materials Technology,

Bremen, Germany) is used [9], which represents the default entry of the SimCarb library. The

carburization depths are evaluated at 0.35 wt.% C in the diagram.

Fig. 21. Measured and simulated carburization profiles (CCD indicated) of experiment N° 1.

As suggested by Fig. 18, the carbon distance curves based on the diffusion coefficients of

Tibbetts and SimCarb Diffusivity for Fe–C austenite reveal a very similar progression with

CCD≈2.2 mm. The SIMS analysis, however, demonstrates a clearly flatter actual carburization

profile. The simulation for the carbon diffusion coefficient in the austenite of 18NiCrMo14-6 steel

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(N° 1) calculated by SimCarb Diffusivity is close to the measurement. With a deviation of about 0.1

mm from the SIMS result of CCD≈1.7 mm, the obtained value of CCD≈1.6 mm can be regarded as

accurate prediction of the carburization depth.

Carburizing Case Hardening Experiment N° 2 on 18NiCrMo14-6

On the example of this experiment, quenching and tempering are also analyzed by measurements

and simulations. Apart from the correspondingly calculated carburization profile at the end of the

boost stage, the depth distributions shown in Figs. 1 and 2 represent fitting curves of the measured

carbon (SIMS) and hardness data.

Carburizing process

The applied two-step treatment is defined in Fig. 22. The time courses of cp and β are plotted.

Fig. 22. Gas carburizing conditions of experiment N° 2, again determined by the foil method.

For both evaluated diffusion coefficients of carbon in austenite, predicted by SimCarb Diffusivity

for the actual chemical composition of the used 18NiCrMo14-6 steel (N° 2 in Table 6) and reported

by Tibbetts for binary Fe–C, an analysis of the effect of the alloy factor ka on the resulting carburi-

zation profile is performed. The SimCarb simulations are displayed in Fig. 23 together with the

SIMS measuring points. The central carbon distributions for the AWT recommendation are respec-

tively enclosed by the limiting concentration profiles based on the minimum (Sauer et al., [28]) and

maximum (Neumann et al., [29]) versions of the SimCarb library of the alloy factor ka.

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Fig. 23. Measured carburization profile of experiment N° 2 and simulations, including ka analysis.

The interpretation of the results summarized in Table 7 proves to be less clear than in experiment

N° 1. However, the alloy dependent carbon diffusion coefficient calculated by SimCarb Diffusivity

again predicts the actual carburization depth of 3.55 mm better than the reference data of Tibbetts.

Table 7. Carburization depth, CCD in mm, for the indicated SimCarb simulations of Fig. 23 based

on the given alloy factors ka. The SIMS carbon measurement provides CCD≈3.55 mm.

Sauer et al., [28] AWT, [9] Neumann et al., [29]

ka=0.9461 ka=1.0371 ka=1.0819

SimCarb Diffusivity 3.07 3.28 3.39

Tibbetts, [8] 4.01 4.27 4.39

The application of Einstein’s equation to the outcome of experiment N° 1, CCD≈1.7 mm, yields

CCD≈3.05 mm for the longer process of Fig. 22 (43.7 h compared to 13.55 h). The actual value of

3.55 mm exceeds this simple estimation, at similar basic carbon contents, by 0.5 mm, which corre-

sponds to an extension of 15 h or 35%. A higher diffusion coefficient in experiment N° 2 is thus

suggested, in agreement with the prediction of SimCarb Diffusivity in Fig. 18.

The influence of the alloy factor on the carbon profile is noticeable. This finding of Fig. 23

underlines the usefulness of supporting steel specific ka measurements, e.g. by means of the foil

method, in case hardening engineering.

Single hardening

After carburizing, the test cylinder is slowly cooled to room temperature (hydrogen desorption

anneal irrelevant in this context). The hardening result is simulated by SimCarb QuenchTemp.

Austenitizing at 830 °C (KASTM=8, relatively fine microstructure) is followed by quenching in still

oil of 53 °C. Final tempering occurs at 200 °C for 2 h. The micro hardness profiles are measured.

The values are converted in HRC in Fig. 24. The case hardening depth at 52.5 HRC, CHD, after

quenching and tempering amounts to 4.24 and 3.85 mm, respectively (cf. Fig. 2). The simulations

are based on the SIMS data of the carbon distribution of Fig. 23 (see diffuse curve in Fig. 1). The

profile of the quenching hardness Hq is well predicted by the software. The corresponding case

hardening depth of 4.02 mm is indicated in Fig. 24.

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Fig. 24. Measured quenching and tempering hardness of experiment N° 2 and corresponding simu-

lations by means of SimCarb QuenchTemp. The case hardening depth, CHD, is indicated.

Calculating the tempering from the simulated quenching hardness provides CHD=3.51 mm. The

difference of 0.34 mm to the measurement result mainly stems from the slightly deviating Hq values

at x≥3.5 mm (see Fig. 24). Using the determined quenching hardness, the SimCarb QuenchTemp

simulation yields visibly better agreement with the experimental tempering data. With CHD=3.75

mm, a deviation of only 0.1 mm remains in Fig. 24.

The volume content of retained austenite, vRA, and the XRD peak width, FWHM, are measured

in the tempered condition. Fig. 25 compares the results with the prediction of the composition of the

microstructure by SimCarb QuenchTemp. The bainite fraction is denoted vB (no pearlite and ferrite).

Fig. 25. Measured retained austenite content and prediction of the microstructural composition by

means of SimCarb QuenchTemp. The obtained XRD peak width profile is also plotted.

The simulated retained austenite amount in the edge zone is just over 5 vol.% lower than deter-

mined. The effect of this deviation on the hardness is evidently small. From 3 mm on, as drawn in

Fig. 25, a slightly increasing volume fraction of bainite, vB, is computed by the software (cf. Fig.

15), which would be suppressed by stronger quenching, e.g., in agitated water. At the tempering

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temperature of 200 °C, only minor retained austenite decomposition occurs. The XRD peak width is

sensitive to the hardness. It is thus reduced by tempering. The decrease of the hardness from a depth

of 2.5 mm on is clearly reflected in the FWHM profile.

Carburizing Experiment N° 3 on 15NiCr13

Fig. 26 reveals the time course of the carbon potential, cp, and the mass transfer coefficient, β, for

experiment N° 3. A conventional two-stage boost-diffuse gas carburizing process is applied.

Fig. 26.

Time course of the carbon

potential and the mass transfer

coefficient in the gas carburizing

process of experiment N° 3,

determined in the boost and diffuse

stage by the foil method.

The virtually coincident carburization profiles simulated by using the diffusion coefficients of

SimCarb Diffusivity for the test steel composition in Table 6 and the Fe–C reference of Tibbetts (see

Fig. 18) agree quantitatively with the SIMS measurement in Fig. 27. The CCD values of both calcu-

lated concentration distance curves are indicated in the diagram.

A comparison of Figs. 21 and 27 provides strong evidence that the diffusion coefficient of car-

bon in austenite depends significantly on the alloy composition of the steel. It is recommended to

include this correlation in the process control of carburizing [30].

Fig. 27.

Measured and simulated carburization pro-

files of experiment N° 3.

An influence analysis of deviations of

the mass transfer coefficient, e.g. due to measurement inaccuracies, is conducted in Fig. 28 for the

carburization profile simulated on the basis of the SimCarb Diffusivity data. A considerable

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variation of the β value of 2.5×10−5

cm/s, determined in both carburizing stages (cf. Fig. 26), of

±1.0×10−5

cm/s is assumed. The resulting small deviation of the carburization depth within ±0.1

mm, indicated in Fig. 28, reflects the fast surface reactions in oxygen-containing industrial gas

mixtures.

Fig. 28. Measured carburization profile of experiment N° 3 and simulation based analysis of the

effect of varying mass transfer coefficient for the CDC predicted by SimCarb Diffusivity.

The result of this evaluation is graphically illustrated in Fig. 29 also for the carbon diffusivity in

austenite according to Tibbetts. The carburization depth is plotted as a function of the mass transfer

coefficient over the whole typical range from 1×10−5

to 4×10−5

cm/s. Conventional carburizing pro-

cesses are mainly controlled by carbon diffusion.

Fig. 29.

Case carburization depth as a function of

the mass transfer coefficient according to

Fig. 28.

Conclusions

Advanced simulation tools provide a deeper insight into the heat treatment processes of steels and

optimization potential in terms of quality and productivity, energy or resource efficiency. The

stand-alone SimCarb software suite for offline computer aided case hardening consists of the three

interacting Windows programs SimCarb, SimCarb Diffusivity and SimCarb QuenchTemp. The

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mathematical analysis of carburizing, quenching and tempering is based on physical material and

process models. The expert software represents the key element of case hardening engineering.

The present paper introduces the program SimCarb Diffusivity. This software provides steel

specific exponentially concentration dependent carbon diffusion coefficients in austenite containing

substitutional alloying elements in the mathematical form of the manual SimCarb input expression

for the simulation of carburization profiles. The implemented quantitative physically based model is

described in detail.

By applying the new tool, the fundamental question is analyzed theoretically and experimentally

of whether low alloying additions of, e.g., Cr, Mn, Mo, Ni or Si around 1 wt.% typical of case hard-

ening materials significantly influence the carbon diffusion coefficient in austenite. A numerical

study of a two-step boost-diffuse gas carburizing treatment of several steels is performed based on

SimCarb Diffusivity calculations. A considerable effect of the alloy composition on the resulting

carbon profile and particularly on the main process target quantity of the carburization depth, taken

at 0.35 wt.% C, is indicated, even within the specification of individual grades.

Three carburizing case hardening experiments are carried out on 18NiCrMo14-6 and 15NiCr13

steel. The carburization profiles are measured by secondary ion mass spectroscopy on polished

microsections of the test cylinders. The evaluation provides strong evidence that the diffusion

coefficient of carbon in austenite depends significantly on the steel composition. It is indicated to

also include this correlation in the computer aided process control of carburizing. The SimCarb

Diffusivity prediction that carbon diffusion occurs faster in the austenite of 15NiCr13 than of

18NiCrMo14-6 is confirmed by the experiments. The hardening processes of quenching and tem-

pering are investigated by means of SimCarb QuenchTemp.

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