A SOLIDIFICATION TIME-BASED METHOD FOR RAPID EVALUATIONOF THE MECHANICAL PROPERTIES OF GREY IRON CASTINGS

P. Ferro , T. Borsato, and F. BonolloDepartment of Engineering and Management, University of Padova, Stradella S. Nicola 3, 36100 Vicenza, Italy

S. PadovanFonderie di Montorso, Via Valchiampo 62, 36050 Montorso, VI, Italy

Copyright � 2018 American Foundry Society

https://doi.org/10.1007/s40962-018-0290-8

Abstract

Designers often need to know the mechanical properties of

different zones of a casting. This is because such properties

are often very different from those declared in the standard

classification of the cast iron used or derived from sepa-

rately cast specimens. At constant chemical composition,

the mechanical properties of a casting will depend on the

microstructure, which in turn is ruled by the cooling rate at

each point of the component. In this work, a method

developed to rapidly predict mechanical properties in each

zone of a cast iron casting, which uses only results from

moulding–solidification numerical simulation, is proposed.

Such approach, applied to real cast irons components, was

found to be in tune with experimental results.

Keywords: grey iron, finite element, thermal analysis,

mechanical properties, EN-GJL 300

Introduction

The mechanical properties of a cast iron component are

often very different from those declared in the standard

classification of the alloy used. This is because a cast iron

grade is classified according to values obtained from sep-

arately casted samples whose thermal and microstructural

history is different from that of the casting itself. As a

matter of fact, geometrical variations of the casting induce

different cooling rates from one area to another, which in

turn are related to different microstructure and mechanical

properties.1,2 For this reason, designers often force the

foundry to obtain castings with controlled mechanical

properties verified with tensile tests, which requires sam-

ples taken from particular zones of the casting itself and not

separately cast. This methodology is cost and time

demanding, since on the one hand, it implies a decrease in

the regular production of castings, on the other, it requires

tensile tests beyond standards that sometimes, according to

the position and thickness of the casting, may be difficult to

obtain. Furthermore, static and fatigue strength of heavy

section iron castings is not standardized yet, and this is the

reason why in recent literature new works were published

about mechanical characterization of such large cast iron

components.3–7

A possible solution to this problem comes from numerical

simulation that is able to predict the mechanical properties

of the casting according to the chemical composition of the

alloy and process parameters.8–11

For instance, Jakob Olofsson and Ingvar L Svensson

demonstrated in their work12 that it is possible to foresee

the mechanical properties of a cast iron component through

casting process simulation and stress–strain simulations. A

particular simulation strategy called ‘a closed chain of

simulations for cast components’,8 which uses solidifica-

tion and solid-state transformation models to predict

microstructure formation and mechanical behaviour on a

local level throughout the component, is proposed. Simi-

larly, two years before, Donlean carried out a model to

predict microstructure and mechanical properties of ferritic

ductile iron components.13 The numerical model was

applied to a heavy section casting with a satisfactory cor-

relation between numerical and experimental results. In the

same year (2000), Italian researchers, Calcaterra, Campana

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and Tomesani, used an artificial neural network-based

system to predict the mechanical properties of spheroidal

cast iron components according to process parameters.14

The required input data are the chemical composition of

the melt, inoculation temperature, time before casting and

diameter of the castings. However, due to the limited

number of specimens considered, the variation range of

tensile strength should be kept below 100 MPa to ensure

effectiveness of the prediction. Furthermore, that approach

does not take into account geometry variations of a real

cast component. Another interesting and recent approach

applied for casting mechanical properties evaluation is the

rapid estimation of mechanical properties of casts through

electrical resistivity measurement.15 However, even if such

approach appears very efficient for a rapid casts diagnostic

on the production line, it requires the determination of a

regression equation for each cast geometry. Finally, a

regression analysis was proposed by Shturmakov and

Loper to predict mechanical properties in commercial grey

iron.16 Unfortunately, the results obtained do not take into

account process parameters and cast geometry but consider

only variations in chemical composition. Mechanical

properties of grey cast iron are well studied in the literature

according to the microstructure variations17 and cooling

rate,18 but only the correlation between hardness and

cooling rate was assessed19 by experiments.

Among the above suggested approaches present in the lit-

erature to predict the mechanical properties of a cast iron

component, the numerical simulation of the casting process

would seem the most promising. However, some draw-

backs must be critically taken into account. Fluid-thermo-

mechanical, transient and nonlinear computation is time

and cost expensive. The high computational cost of a

model is often not suitable for industrial applications that

require fast and reliable solutions. Model reliability is

related to the correctness of input data that are often dif-

ficult to find such as metallurgical, thermal and mechanical

properties as a function of temperature, thermal resistance

at the interface between mould and casting. Another

method is based on the mechanical properties evaluation as

a function of the section thickness20 and/or microstruc-

ture.21 In this context, the present work is aimed to propose

an approach that exploits the advantages of numerical

thermal simulation and overcomes the problems related to

the micro- and macro-mechanical computation. The

solidification time is first obtained at each point of the

casting by means of moulding and thermal numerical

simulation, which is known to be fast and reliable.22

Mechanical properties of the component are then calcu-

lated by using a master curve that correlates the ultimate

tensile strength (UTS) with the solidification time.

Casting Mechanical Properties Prediction Strategy

The proposed approach, aimed to predict the mechanical

properties of iron castings, is shown in Figure 1. A ‘master

curve’ is calculated for each alloy composition starting

from tensile tests carried out with specimens coming from

step-form samples. An experimental curve describing the

variation of the ultimate tensile strength versus the thick-

ness is first obtained. By correlating the solidification time

with the step thickness, through a validated numerical

simulation, the master curve, UTS versus solidification

time, is then obtained.

The master curve is used to rapidly predict the mechanical

properties of the casting. As a matter of fact, it is quite

simple to simulate the moulding and solidification by

means of numerical simulation. The solidification time in

each specific area of the casting, obtained by the thermal

simulation, is used as input for the prediction of the cor-

responding mechanical properties with an uncertainty that

is directly correlated with the scatter band of the master

curve itself (Figure 2). In the following paragraphs, the

Figure 1. Schematic of master curve calculation.

International Journal of Metalcasting

procedure and results obtained for the calculation of the

master curve of the grey cast iron EN-GJL-300, whose

chemical composition is summarized in Table 1, are

described.

Master Curve of EN-GJL 300 Cast Iron

Samples Definition

The geometry of the samples was obtained according to

both UNI EN 1561 Standard and the work carried out by

Behnam et al.19 In particular, the material was the EN-GJL

300 grey cast iron (GCI), and the steps thickness and

specimens diameter were chosen according to the relevant

wall thickness defined in the above-mentioned standard.

Figure 3 shows the mould and samples geometry used in

the experiments.

It is important to note that in order to take into account the

effects of different cooling rates within the same thickness,

a sample is obtained from the 30-mm-thickness step

directly ahead the gate in step-form sample B (Figure 3).

Furthermore, in order to dampen the bias due to variables

of foundry parameters that may occur in different working

days, four samples a day were cast over four different days.

The castings used to verify the proposed approach were a

valve housing and a front cover produced according to the

UNI EN 1561 Standard. The required Brinell hardness of

the fist casting had to fall in the range between 200 and 250

HB, while the tensile strength had to be minimum

250 MPa. The second casting had to fulfil a Brinell hard-

ness of 200-230 HB and a minimum UTS of 210 MPa.

Both castings were produced with the EN-GJL 300 GCI.

Figure 4 shows the geometry of the real castings used to

validate the proposed approach and the areas where the

samples for tensile tests were taken from.

Temperature Measurement

In order to calibrate the numerical model, temperature

measurement was carried out directly inside the casting

during the moulding and solidification by means of a

K-type thermocouple (K chromel (Ni–Cr)(?)/alumel(Ni–

Al)(-)). With the purpose to protect it from the melted

alloy, the thermocouple was inserted in a ceramic pipe

(Al2O3) and locked within, through a subsequent pipe

filling with an alumina-based ceramic solution, then dried.

Figure 2. Casting tensile strength prediction methodology.

Table 1. Chemical Composition of the Grey Cast Iron EN-GJL-300 (wt%)

C Si Mn Cu Cr Ni Mo Sn S

3.12–3.2 1.65–1.77 0.721–0.824 0.314–0.366 0.15–0.194 0.028–0.038 0.004–0.0078 0.054–0.067 0.078–0.092

International Journal of Metalcasting

Figure 5 shows the location of the thermocouple that was

in the middle of the 30-mm-thickness step of sample A

(Figure 3), while Figure 6a shows the obtained result and

the definition and procedure used to calculate the solidifi-

cation time.

Numerical Model and Parameters Calibration

The numerical model for moulding and solidification

simulation was carried out by NovaFlow & Solid. In the

proposed approach, the simulation is used to calculate the

solidification times in the different parts of the casting

whose mechanical properties have to be determined. It is

mandatory that input parameters used in the model have to

be correct and thus validated by experiments. A conver-

gence analysis, resulting in a 4.7-mm element size

dimension, was performed in order to optimize the mesh

density and the corresponding computational times. The

parameters calibration of the model was obtained by

comparing the thermal history measured with the

Figure 3. Mould and samples geometry used in the experiments (mm). Specimenswith the same colour have the same diameter highlighted in the legend.

Figure 4. Valve housing (a) and front cover (b).

Figure 5. Thermocouple location.

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thermocouple and the one resulting from the simulation.

Input parameters were taken from the software database,

and little variations (with reference to above all thermal

contact resistance at interface between the mould and the

molten metal) were made necessary in order to overlap the

two curves as shown in Figure 6b. The alignment between

experimental and numerical results assured a good cali-

bration of the model parameters.

Results and Discussion

UTS Versus Thickness

Figure 7 summarizes the tensile test results (in terms of

UTS) as a function of the thickness (or diameter) of the

sample where specimens were taken from. It is noted that

despite the scattering of the results, typical of the analyzed

quasi-brittle material, an inverse relation is found between

thickness/diameter of the sample and its UTS value. The

greater the thickness/diameter, the lower the tensile

strength. However, this relation seems not to be true for

samples taken from 10-mm steps. This apparent anomalous

behaviour is due to the high cooling rate and the conse-

quent overcooled microstructure that can be detected in

that zone of the step-form sample. It is worth mentioning

that the scattering of results is also due to the different

microstructure that may be present in the specimens taken

from the same step thickness but with different solidifica-

tion times. For the same reason, the microstructure, and

thus the mechanical properties, of specimens coming from

step-form and cylindrical form samples will be different.

This is the main reason why the ‘master curve’ must refer

to the solidification time rather than the casting thickness or

diameter as made in the past or standards.

Master Curve, UTS Versus Solidification Time

By using the calibrated numerical model, the solidification

time was calculated for each specimen by referring to the

central point of the step where it was taken from. The little

variation of solidification times across the section of the

specimen was thus neglected. By using only UTS values

coming from step-form samples, Figure 7 is thus converted

in a more useful plot, UTS versus solidification time,

named ‘master curve’ (MC) (Figure 8). Data were statis-

tically elaborated by using a lognormal distribution, and

survival probabilities (the probability that the sample

will break at lower load values) (Ps) of 50%

(UTS = 2341.5t-0.348) and 95% (UTS = 1899.5t-0.348)

were obtained (Figure 8).

Figure 6. Measured temperature history inside the30-mm thick step (a) and comparison between experi-mental and numerical (FEM) temperature history evalu-ation (b).

Figure 7. UTS of EN-GJL 300 GCI as a function of sample thickness/diameter.

International Journal of Metalcasting

It is observed that despite the three thickness values of the

step-form moulds and because of the different position of

the 30-mm-thick step in the step-form mould A compared

to that in the step-form mould B (Figure 3), different

solidifications times were calculated, as expected

(Figure 8).

Rapid Calculation of Casting’s UTS

Assuming that sound castings without relevant macro-de-

fects are obtained, the MC (Figure 8) can be used to

rapidly estimate the UTS of the casting by using the

solidification times coming from simulation. By referring

to the calculated solidification times of the zones of interest

of the front cover and valve housing shown in Figure 4, the

estimated UTS values with a survival probability of 50%

were 272 MPa, 260 MPa and 217 MPa, respectively. If the

real UTS values obtained by means of tensile tests are now

inserted in the MC, it is easy to observe that they fall over

the calculated survival probability of 95% and in particular

they are close to the Ps 50% values (Figure 9) (Table 2).

The same results were obtained for the mechanical

Figure 8. Master curve of EN-GJL 300 GCI.

Figure 9. Comparison between predicted and experimental UTS values as afunction of the solidification time.

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properties of the cylindrical form samples, as shown in

Figure 9. Finally, Figure 9 also shows the obtained

microstructures as a function of the solidification time. The

lower the solidification time, the higher the graphite

coarsening linked to a UTS reduction. However, at solid-

ification times lower than about 187 s, the overcooled

microstructure as well as the finer pearlitic matrix makes

the material more brittle and sensitive to defects. (The

scattering of results increases.) This is also confirmed by

‘hardness versus thickness’ relation with Brinell hardness

measured values of 193, 215 and 230 corresponding to

thicknesses of 30 mm, 15 mm and 10 mm, respectively.

Conclusions

A method was described for a rapid mechanical properties

estimation of different zones of a casting made of EN-GJL

300 grey cast iron. The proposed approach is based on the

intrinsic relation between microstructure-solidification

time and mechanical properties. A master curve, describing

the tensile property as a function of the solidification time,

was calculated by means of tensile tests performed with

specimens taken from step-form samples and moulding–

solidification numerical simulation. The main idea is that

for sound castings without relevant solidification defects,

the master curve depends only on the chemical composi-

tion of the analyzed alloy. As a matter of fact, for each cast

iron composition, the solidification time is the most pow-

erful parameter influencing the microstructure. Process

parameters such as casting temperature, inoculation and

thickness influence the solidification time that is just

included in the master curve. It was demonstrated that with

a rapid numerical calculation of the solidification times of

the zones of interest of an industrial casting, the master

curve is able to rapidly estimate the ultimate tensile

strength of such zones. The advantage of the method

consists in overcoming the problems related to mechanical

numerical computation (high computational times, uncer-

tainties about the mechanical properties of the alloy as a

function of phases and temperature) and exploiting only

results coming from the thermal numerical computation,

which, moreover, becomes a standard practice in modern

foundries. Further experimental investigations could

improve the master curve shape and reduce the scattering

of results.

Acknowledgements

The authors would like to thank Fonderie di MontorsoSpA for the supply of the material, as well as thefinancial and technical support to this project.

Table 2. Comparison Between Experimental and Numerical Results

Sample Solidification time(s)

UTS experimental(MPa)

UTS (mean value)(MPa)

UTS (predicted, Ps 50%)(MPa)

Error(%)

Front cover 572 249.4 260.1 256.9 1.1

572 252.1

572 271.2

572 271.4

572 263.9

572 252.3

Front cover 987 189.5 217.4 212.5 2.2

987 208.6

987 228

987 208.9

987 226.1

987 239.2

987 221.8

Valvehousing

562 253.3 271.6 258.6 5

562 265.6

562 273.3

562 289.3

562 261.5

562 276.5

562 282

International Journal of Metalcasting

Compliance with Ethical Standards

Conflict of interest The authors declare that they have no conflict

of interest.

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