Effect of melt cleanliness on the formation of porosity defectsin automotive aluminium high pressure die castings

C. Tiana,*, J. Lawb, J. van der Touwc, M. Murraya, J.-Y. Yaod, D. Grahamd, D. St. Johnd

aCSIRO Manufacturing Science and Technology, Preston, Vic., AustraliabNissan Casting Australia Pty Ltd., Dandenong, Vic., Australia

cCSIRO Mathematical and Information Science, Clayton, Vic., AustraliadCAST/The University of Queensland, St. Lucia, Qld, Australia

Received 2 September 1999; accepted 23 November 2001

Abstract

The effect of melt cleanliness on the formation of porosity defects in automotive aluminium high pressure die castings (TA Transmission

Case) was investigated experimentally. The experiments were conducted under actual industrial production conditions at Nissan Casting

Australia Pty Ltd. (NCAP). It was found that the probability of rejection due to excessive porosity present at critical locations in the castings

(determined using a real-time X-ray radiographic method) increased as the number of inclusions in the melt (measured with LiMCA II)

increased. The types of inclusion in the melt were identified as mainly amorphous oxides, oxide films and sludge particles. # 2002 Published

by Elsevier Science B.V.

Keywords: Porosity; Casting porosity; High pressure die casting; Melt cleanliness; Inclusions

1. Introduction

Porosity in cast automotive components is one of the

major quality problems facing high pressure die casters.

This is because porosity often causes leaking problems,

surface defects and machining problems. Depending on

its functionality, each type of casting has its own tole-

rance limits on porosity levels at certain locations in the

casting. Porosity in high pressure die castings is usually

classified as gas porosity, shrinkage porosity and flow

porosity, all of which are intrinsic problems associated

with the process.

Gas porosity is believed to be caused mainly by trapped

air, steam and burning products of organic lubricants used

in the shot sleeve. Shrinkage porosity occurs when the gate

area (from which the molten metal is injected into the

die cavity) solidifies before solidification in other areas of

the casting is completed (since in this case, the passage for

molten metal to feed the shrinkage due to solidification is cut

off). Thermal conditions of the die (dictated by its water

cooling system, the spray settings, the thermal conductivity

of the die material, the cycle time, etc.) and the temperature

of the molten metal are the major factors affecting the

amount of shrinkage porosity for a given alloy composition

and casting geometry. Flow porosity results from insufficient

pressure toward the end of cavity filling. Factors affecting

the porosity levels in the castings can be numerous. For

example, any factors affecting the fluid flow conditions

during cavity filling (such as the moving speed of the piston

in the shot sleeve, the velocity of the molten metal flowing

through the gate, the geometry and the location of the gate

for a given casting, pressure applied, etc.) may potentially

affect the amount and/or distribution of entrained air.

Melt cleanliness (i.e. inclusions and dissolved hydrogen

in the melt) may also have an effect on porosity formation in

high pressure die castings. Die casters have found by

experience that the porosity levels often increase when

100% returns (scrap) are used constantly for a period with-

out adequate treatment. The problem could become so

severe that up to 80% of the castings made from all casting

machines fed this type of melt failed to meet the quality

requirement due to excessive porosity present in the cast-

ings. Such a situation has been termed a porosity outbreak

[1]. In order to identify the cause of the porosity outbreak

and eventually prevent it from recurring, NCAP initiated a

research project with CSIRO (Commonwealth Scientific and

Industrial Research Organization)/CAST (Cooperative

Research Centre for Alloy and Solidification Technology)

to investigate the possible link between molten metal quality

and the quality of high pressure die castings.

Journal of Materials Processing Technology 122 (2002) 82–93

* Corresponding author.

0924-0136/02/$ – see front matter # 2002 Published by Elsevier Science B.V.

PII: S 0 9 2 4 - 0 1 3 6 ( 0 1 ) 0 1 2 2 9 - 8

2. The process

In high pressure die casting, the metal is forced into a steel

cavity (or die) through a narrow orifice (or gate) at speeds

ranging between 20 and 100 m/s. This is achieved by a

piston and a cylinder (or shot sleeve) where the piston is

driven by a high pressure hydraulic circuit capable of

achieving metal pressures of 100 MPa. The subsequent

casting is solidified under pressure with the aim of ‘feeding’

the casting with molten metal from the gate.

3. The premise

The effect of melt cleanliness on porosity formation in

castings is mainly manifested in the following ways: (a)

inclusions in the melt tend to impair the fluidity of the

molten metal [2], thus hindering feeding through the inter-

dendritic regions to difficult-to-access areas; (b) inclusions

in the melt tend to act as nucleation sites for dissolved

hydrogen to precipitate, thus increasing the melt sensitivity

to gas porosity [3–5]; (c) dissolved hydrogen in the melt

tends to precipitate out of solution to form hydrogen gas

bubbles during solidification due to its much lower solubi-

lity in solid than in liquid aluminium [6,7]. These effects are

well recognized in gravity castings. In high pressure die

casting, although these effects may, by intuition, not con-

stitute the major cause of the total porosity, they may result

in an additional effect thereby causing the permissible

porosity limit to be exceeded for a given situation. In

addition to the mechanisms mentioned above, the non-

wetting nature of oxide inclusions may facilitate large air

bubble formation which may otherwise, for the same

amount of entrained air, remain as dispersed smaller bub-

bles (the size of pores in automotive die castings is a major

criterion for quality control). In some thicker sections, if a

reduced or negative pressure is created due to solidification

shrinkage, the dissolved atomic hydrogen may come out

of solution to form hydrogen gas which then occupies the

shrinkage space and expands under high temperature

thereby aggravating the porosity in these areas. It has been

shown that the presence of excessive dissolved hydrogen in

the melt increased the total porosity (based on density

measurement) of a given casting by 60% (from 0.5 to

0.8%) [8].

The anecdotal increase in porosity when metal produced

from 100% returns are used indicates that something must be

abnormal in the melt (since this has been known to occur

over a number of machines in one plant at the same time, it is

unlikely that this event was due to localized deviation in

machine variables). The abnormalities may include varia-

tions in inclusion content, dissolved hydrogen and chemical

composition.1 In the present work, the effect of inclusion

content in the melt on the formation of porosity defects in the

castings was examined.

4. Experimental

4.1. The casting chosen for experimentation

In order to determine if there was a link between inclusion

level and the occurrence of excessive porosity (i.e. the

formation of sufficient porosity in critical locations to

warrant the rejection of the casting), a ‘TA Transmission

Case’ casting (see Fig. 1) with a relatively complex geo-

metry and sensitive to porosity formation was chosen. The

part is made in a fully instrumented high pressure die casting

machine so that any deviations in machine parameters could

be determined. Since casting geometry is an important

variable that affects the fluid flow and solidification char-

acteristics and thus porosity formation, all the experiments

were conducted with the same casting.

4.2. Melt cleanliness measurement

The alloy used was a Japanese standard aluminium die

casting alloy—ADC12 whose chemical composition is

similar to the Australian standard—AA335, British stan-

dard—LM2 and American standard—AA383, respectively.

The properties of AA383 aluminium alloy can be found

elsewhere [9]. The inclusion content in the melts was

measured using industry standard methods:

� LiMCA (liquid metal cleanliness analyser) [10] which is

capable of measuring particles greater than 20 mm in

molten aluminium in terms of number and size distribution.

� PoDFA (porous disc filtration apparatus) [11] was used to

assist in determining the type of inclusions by collecting

Fig. 1. A TA Transmission Case casting.

1 Although the melt is checked to ensure the alloy specification is met,

some uncontrolled minor and/or trace elements may vary.

C. Tian et al. / Journal of Materials Processing Technology 122 (2002) 82–93 83

the particles present in the molten metal on to the surface

of a fine ceramic filter.2 Approximately 1–2 kg of molten

metal was passed through the filter under pressure. Then,

the used filter was cut and polished and examined micro-

scopically. The types of particles present can be identified

either using optical microscopy based on their colour,

shape as well as morphology or, for some unknown types,

using scanning electron microscopy (SEM) or transmis-

sion electron microscopy (TEM) based on their X-ray

spectra and/or diffraction patterns.

The melts with different levels of cleanliness used for the

experiments were (a) normal melts tapped from a holding

reverberatory furnace which feeds all the high pressure die

casting machines, (b) dirty melts made of 100% returns and

(c) melts made of returns and ingots, melted in reverberatory

furnaces.

The contents of dissolved hydrogen in these melts were

assumed to be similar, based on the fact that all the melts

were prepared in a similar manner using natural-gas-heated

reverberatory furnaces.

The alloy composition was checked using conventional,

spark-emission spectrometry.

4.3. Experimental procedures

All the experiments were conducted using a 1250 t

locking-force, UBE high pressure die casting machine at

NCAP.

4.3.1. Small-scale batch experiments

To observe the trends in porosity formation when chan-

ging the level of inclusions, a series of small-scale batch

experiments were conducted using melts of varying inclu-

sion levels. In these trials, the experimental molten metal

held in a designated small holding furnace or a transfer ladle

was manually transferred to the casting machine during its

normal production operation. The particle levels in the melts

were measured with LiMCA II just prior to each trial.

PoDFA samples were also taken. The temperature of the

experimental metal was controlled at 640 8C, the same as

that of normal production melts. The experimental castings

along with six normal castings from production (three

immediately before and three immediately after the trial)

were selected for porosity examination. Since the experi-

mental castings were made during normal production, all

parameters except melt cleanliness should be the same as

those of normal production. Hence, the change in the amount

and distribution of porosity in these experimental castings

compared to those in the normal castings could be linked to

the melt cleanliness.

4.3.2. Large-scale batch experiments

Measurable effects of inclusions on porosity formation

were evidenced from the small-scale trials. In the large-scale

batch trial, the experimental metal was directly poured into

the holding furnace from which the molten metal was

automatically ladled into the casting machine. All para-

meters (except melt cleanliness) were maintained the same

as those of normal production. The particle level in the melts

was continuously measured with LiMCA II in the holding

furnace while the machine was running. A total of 49

castings were made during this trial.

4.3.3. Large-scale consecutive trials

The experimental procedure was the same as that men-

tioned in Section 4.3.2, however, the experiments continued

by consecutively re-filling the holding furnace when the melt

level in the holding furnace dropped to a certain level. Here

the experimental metal containing different levels of inclu-

sions was used to re-fill the furnace. The holding furnace was

re-filled eight times and a total of 240 experimental castings

were made.

4.4. Measurement of machine operating parameters

In the above trials, all the parameters, except the inclusion

content of the molten metal, were not intentionally changed.

However, the actual value of a given parameter may fluctuate

around its set value. This raised concerns as to whether the

rejected experimental castings due to excessive porosity were

caused by the ‘dirty’ metal or by variations of other para-

meters within their normal fluctuation range. For this reason,

in the large-scale consecutive trials, the principle machine

operating parameters (e.g. gate velocity, fill time, cycle time,

etc.) were monitored using a shot monitoring system con-

nected to the casting machine. The surface temperature of the

die was measured with an infra-red thermographic camera.

Fig. 2. An X-ray radiograph showing the porosity in a specific location of

a casting.

2 The need for collecting the inclusions in the melt to a concentrated area

(i.e. on the top of the PoDFA filter) for the purpose of examination arises

because the inclusion content in aluminium melts is normally too low to

allow a direct examination of a melt sample. Despite the low content, the

harmful effects of these inclusions can still materialize in most aluminium

fabrication processes and end products.

84 C. Tian et al. / Journal of Materials Processing Technology 122 (2002) 82–93

4.5. Casting quality assessment

4.5.1. Real-time X-ray inspection

All the castings made in the trials were examined using a

real-time X-ray radiographic inspection system (which is

routinely used by NCAP for normal production inspection)

to reveal the porosity in the castings (see Fig. 2). NCAP

classifies the porosity at a given location into four grades

according to its severity: a grade of ‘1’ denotes nil or little,

‘2’ some, ‘3’ considerable (or just acceptable), and ‘4’ not

acceptable. If the X-ray grade at one location in a casting

scores a 4, the casting will be rejected (there are 31 locations

that need to be inspected in the TA Transmission Case

casting). The accuracy and consistency of the X-ray grading

depend on the experience of the inspector.

A casting is rejected if the size of the pores at given

locations exceeds the prescribed limits set out by the cus-

tomer. The tolerance limit varies with locations in the same

casting. Some locations cannot tolerate any observable

porosity, while other locations may tolerate pores up to

several millimetres. It is the porosity at certain locations

in the casting that is of concern rather than the total

porosity.3 For this reason, the term ‘excessive porosity’

was used to indicate a level (at a given location) that requires

rejection of the casting.

4.5.2. Microscopic examination

In order to fully characterize the nature of the porosity,

detailed microscopic examination was also conducted. In

these analyses, the castings were cut up at locations of

interest, and the number, size, shape and morphology of

the pores at each location were measured using the X-ray

radiographic method. Metallographic examination was also

conducted.

5. Results and discussion

The experimental results in terms of reject rates as

determined by the X-ray grades vs. inclusion content are

presented in Fig. 3 for the small-scale batch experiments.

Fig. 4 shows the results for the large-scale consecutive

experiments together with the large-scale batch data point.

The combined data is presented in Fig. 5. The size of the

circles in these plots is proportional to the number of

experimental castings made.

From these results there appears to be a relationship

between inclusion level and reject rate. In order to deter-

mine whether the relationship is statistically valid, the

following statistical analysis was undertaken. It was found

that the data could be fitted into the following logistic

equation:4

pðxÞ ¼ eyðxÞ

1 þ eyðxÞ ; (1)

where p(x) is the probability of rejection, x the inclusion

content in terms of thousand particles (�20 mm) per kg of

molten metal (k/kg), and yðxÞ ¼ aþ bx.

Fig. 3. Reject rate vs. number of inclusions in the melts. Data obtained from the small-scale batch experiments.

3 A casting with a higher total porosity may be accepted, while a casting

with a lower total porosity may be rejected, depending on the distribution

of the porosity within the casting.

4 Logistic transformation is often used in statistics to map the (0, 1)

probability range onto the real line (�1, þ1) so that a correlation can be

investigated in a manner analogous to standard linear regression.

C. Tian et al. / Journal of Materials Processing Technology 122 (2002) 82–93 85

The coefficients a and b were estimated from the data

using the method of maximum likelihood. The fitted equa-

tions based on the experimental data were

yðxÞ ¼ �2:98 þ 0:0500x ðsmall-scale batch experimentsÞ;(2)

yðxÞ ¼ �5:02þ 0:0818x ðlarge-scale consecutive experimentsÞ;(3)

yðxÞ ¼ �4:08 þ 0:0617x ðcombined dataÞ: (4)

The fitted equations are shown in Figs. 3–5. The shaded

areas give 95% confidence intervals for the fitted equations.

The single data point from the large-scale batch experiment

shown in Fig. 4 was not taken into account during the

regression analysis (since it is a separate event). As can

be seen, this single data point is indeed consistent with the

set of data from the large-scale consecutive experiments,

Fig. 4. Reject rate vs. number of inclusions in the melt. Data obtained from the large-scale consecutive and batch experiments. The sizes of the circles

indicate the number of castings made for the corresponding data points.

Fig. 5. Reject rate vs. number of inclusions in the melt. Combined data from the consecutive and batch experiments. The size of the circles indicates the

number of castings made for the corresponding data points.

86 C. Tian et al. / Journal of Materials Processing Technology 122 (2002) 82–93

Fig. 6. Schematic drawings of the TA Transmission Case that show the zones where X-ray examinations were performed. There are 10 positions in zones A

and D, three in B and five in C, respectively.

Fig. 7. Porosity index (mm3, averaged from three castings) of: (a) normal castings; (b) castings made from a dirty melt, at locations where porosity must be

controlled according to prescribed standards (‘zone’ and ‘position’ denote specific locations in the castings).

C. Tian et al. / Journal of Materials Processing Technology 122 (2002) 82–93 87

indicating that batch experiment is as a valid approach as

consecutive experiments.

It is evident from these figures that the results obtained

from the small-scale batch and large-scale batch and con-

secutive experiments are consistent. Judging from the fact

that the correlation between inclusion content and prob-

ability of rejection was statistically significant, p < 0:05,

based on a standard statistical test using generalized linear

regression [12], it can be concluded that the probability of

rejection will increase with increasing inclusion content.

Eq. (1) is not intended to represent a theoretical relationship

between the probability of rejection and the inclusion content.

Rather, it merely represents an empirical relationship between

the two variables for the given casting and experimental

conditions in much the same way that a straight line is used

to illustrate a relationship between two correlated measured

variables. The logistic equation (1) is a generalization of a

linear equation to the case where one of the variables is a

probability rather than a directly measured variable. Note that

the logistic equation (1) can be re-written as

logpðxÞ

1 � pðxÞ

� �¼ aþ bx: (5)

Thus, the log of the odds against rejection is assumed to be

linearly related to the inclusion content.

The statistical nature of the effect of inclusions in the melt

on the reject rate of high pressure die castings due to the

formation of excessive porosity stems from the random nature

of inclusion distribution in the castings and the inadequate

account for the nature of inclusions as independent variables.

Fig. 8. The difference in porosity index between normal castings and castings made from a dirty melt.

Fig. 9. X-ray indices of three experimental and six normal castings.

88 C. Tian et al. / Journal of Materials Processing Technology 122 (2002) 82–93

For a given number of inclusions, depending on where the

inclusions end up in the casting, the effect on porosity

formation at certain locations would be different. The effect

of inclusions on porosity formation depends not only on the

number, size, and spatial distribution but also on the nature

of inclusions such as type (which, for example, may affect

wetting) and shape or morphology. For example, pore nuclea-

tion should be easier on non-wetting and/or sharp-edged

particles than on wetting and/or smooth spherical particles.

Among these variables, only the number, size (usually the

equivalent spherical diameter), and type of the inclusions can

be measured with a certain degree of success (e.g. using

LiMCA II and PoDFA). To use the total number of inclusions

greater than 20 mm in the melt as an independent variable to

assess the effect of inclusions on porosity formation in a

casting is apparently inadequate. (Due to the inherent diffi-

culties in measuring the various attributes of the inclusions in

molten metal, it is impractical to account for all attributes

as necessary independent variables.) That is why the results

were treated as probability events rather than deterministic

quantities.

In order to verify the result of the statistical analysis, two

additional approaches were taken to obtain supporting evi-

dence of the effect of melt cleanliness. Firstly, the castings

were characterized using a porosity index which is the

volume of porosity in mm3 estimated from the microscopic

X-ray examinations conducted at a number of locations in

the casting (see Fig. 6). Fig. 7 shows the porosity index at

Fig. 10. Cycle time of: (a) all castings; (b) rejected castings made in the consecutive large-scale experiments. The prolonged cycle times are associated with

minor stops.

C. Tian et al. / Journal of Materials Processing Technology 122 (2002) 82–93 89

different locations in: (a) normal castings and (b) castings

made from a dirty melt (80,000 particles greater than

20 mm/kg of molten aluminium), while Fig. 8 shows the

difference between the two at their corresponding locations.

It is clear that the porosity levels in the castings made from

the dirty melt increased at most locations compared to those

in the normal castings.

The second approach was to calculate X-ray indices. The

X-ray index is defined as the averaged X-ray grades at all

inspected locations in a given casting. Fig. 9 presents the

X-ray indices of nine castings (three experimental along

with six normal castings immediately before and after a

small-scale batch trial) made in a sequential order. It is

evident that the X-ray indices increased when the dirty

experimental melt was used in the trial.

To determine whether the cause of rejects is related to

deviations in operating conditions, plots were made of various

casting parameters vs. the casting number for the castings

made in the large-scale consecutive trial (Figs. 10–12). In each

case, the plot labelled (a) is for all the castings made, whereas

(b) is only for the rejected parts. It is interesting to note that

most of the rejected castings were not directly associated with

minor stops (i.e., prolonged cycle times caused by interrup-

tions of furnace re-fills, extractor or spray arm malfunctions,

etc.), and their corresponding gate velocities and fill time fell

randomly within the normal fluctuation range. The die surface

temperature from shot to shot was also relatively constant.

This appears to indicate that the rejected castings were not

caused by the normal fluctuations of the principle operating

parameters monitored during the experiments.

Fig. 11. Average gate velocities of: (a) all castings; (b) rejected castings made in the consecutive large-scale experiments.

90 C. Tian et al. / Journal of Materials Processing Technology 122 (2002) 82–93

The inclusions found in the PoDFA filter cakes were

examined and identified using optical microscopy, SEM

and TEM. It was found that the inclusions in the melts

consist mainly of amorphous oxides [13], oxide films and

sludge particles (see Fig. 13).

Since casting geometry is an important factor that affects

the fluid flow and solidification processes (which in turn

affect the formation of porosity in the casting), the results

obtained in this study cannot be generalized to apply to other

casting geometries or situations where the operating para-

meters are different from the ones used in the experiments

conducted in this study. Thus, it is the trend that is more

meaningful rather than the actual values. A general approach

would require identification of all relevant dimensionless

groups of all independent variables and, in some cases, the

satisfaction of geometric similarity which is impossible to

realize for different casting geometries.

6. Summary

The effect of melt cleanliness on the formation of

excessive porosity leading to rejection of an aluminium

high pressure die casting (TA Transmission Case) was

investigated experimentally. The results showed that the

reject rate increased with an increase in inclusion content

Fig. 12. Fill time of: (a) all castings; (b) rejected castings made in the consecutive large-scale experiments.

C. Tian et al. / Journal of Materials Processing Technology 122 (2002) 82–93 91

in the melts. The types of inclusion present in the melts

were found to consist of mainly amorphous oxides, oxide

films and sludge particles. The results obtained in this study

indicate that it is necessary to control melt cleanliness in

order to reduce the reject rate of structural, automotive

aluminium high pressure die castings, thus improving

productivity.

Acknowledgements

The authors would like to thank Peter Miller for the

identification of the amorphous oxides and Necip Kultur

and Stefan Gulizia for their assistance in carrying out the

plant trials. The authors would also like to thank the NCAP

staff for their assistance throughout the work. Finally, the

Fig. 13. Inclusions found in a filter cake formed from filtering a dirty melt (80,000 particles greater than 20 mm/kg of molten metal) with PoDFA. Top:

optical image; bottom: backscattered electron image (the bright phase is a sludge particle, the black particles are amorphous oxides).

92 C. Tian et al. / Journal of Materials Processing Technology 122 (2002) 82–93

authors would like to express their sincere gratitude to Trust

Bank for funding this research.

References

[1] K. Hooper, Private communications, 1997.

[2] D.E. Groteke, Influence of SNIF treatment on characteristics of

aluminum foundry alloys, AFS Trans. 93 (1985) 953–960.

[3] K.J. Brondyke, P.D. Hess, Interpretation of vacuum gas test results

for aluminum alloys, Trans. TMS–AIME 230 (1964) 1542–1546.

[4] E.L. Rooy, E.F. Fischer, Control of aluminum casting quality by

vacuum solidification tests, AFS Trans. 76 (1968) 237–240.

[5] D.E. Groteke, Testing for molten metal cleanliness, Die Casting

Engr. (September/October 1996) 18–26.

[6] P.N. Anyalebechi, Analysis and thermodynamic prediction of

hydrogen solution in solid and liquid multicomponent aluminum

alloys, Light Met. (1998) 827–842.

[7] C.E. Ransley, H. Neufeld, The solubility of hydrogen in liquid and

solid aluminum, J. Inst. Met. 74 (1948) 599–620.

[8] C. Tian, J. Law, M. Murray, Unpublished work, 1998.

[9] Properties of cast aluminum alloys, in: ASM Handbook Committee

(Eds.), Metals Handbook, Vol. 2, 9th Edition: Properties and

Selection: Nonferrous Alloys and Pure Metals, American Society

for Metals, Metals Park, OH, 1979, pp. 152–179.

[10] R.I.L. Guthrie, D.A. Doutre, On line measurements of inclusion in

liquid metals, in: Proceedings of the International Seminar on

Refining and Alloying of Liquid Aluminum and Ferro-alloys,

Trondheim, Norway, 1985, pp. 145–163.

[11] D. Doutre, B. Gariepy, J.P. Martin, G. Dube, Aluminium cleanliness

monitoring: methods and applications in process development and

quality control, Light Met. (1985) 1179–1195.

[12] P. McCullagh, J.A. Nelder, Generalized Linear Models, Chapman &

Hall, London, 1983.

[13] P. Miller, CSIRO Internal Reports, CSIRO Manufacturing Science

and Technology, Clayton, Vic., 1997.

C. Tian et al. / Journal of Materials Processing Technology 122 (2002) 82–93 93