JOM • June 201128 www.tms.org/jom.html

Research Summarycorrosion in Biological environments

performance-driven design of Biocompatible mg alloys

Nicholas T. Kirkland, Mark P. Staiger, David Nisbet, Chris H.J. Davies, and Nick Birbilis

How would you……describe the overall signifi cance of this paper?Bioresorbable magnesium (Mg) al-loys represent a potential revolution in orthopedic surgery. Although Mg alloys have been investigated for this role with some intensity over the past decade, very little is known about the mechanistic aspects that relate their performance in various key properties simultaneously—in realistic in vitro environments. This paper explores the mechanical and biocorrosion performance of a range of alloys containing only biocompat-ible elements.

…describe this work to a materials science and engineering profes-sional with no experience in your technical specialty?It is understood that the mechanical performance of an alloy is dictated by microstructure; however, so is its corrosion rate. These properties are nominally inversely correlated. Adequate control of these properties is crucial to the successful imple-mentation of Mg alloys in the body, where failure is not an option. In this work 19 alloys were investigated using immersion, electrochemical, and compression experiments. Their performance was connected to their alloying, and any trends in the data due to the contribution of an indi-vidual element are explored.

…describe this work to a layperson?Mg alloys provide several benefi ts over current biomedical metallic implant materials, the most im-portant of which is their ability to biodegrade in the body—removing the need for a second operation. For an implant to be successful, it is necessary to fully understand its cor-rosion and mechanical properties. This work looks at the performance of a number of biocompatible alloys, discusses the reasons for any notice-able trends, and provides a model which can be used to predict how uninvestigated alloys may perform.

Magnesium (Mg) and its alloys pro-vide numerous benefi ts as a resorptive biomaterial and present the very real possibility of replacing current metallic implant materials in a variety of roles. The development of suitable biode-gradable implant alloys is a multidis-ciplinary challenge, since alloy design must be confi ned to a range of alloying additions that are biologically nontox-ic, whilst still providing the requisite mechanical properties. This leaves a small number of compatible elements that can provide benefi ts when alloyed with Mg, including calcium (Ca) and zinc (Zn). To date, although a range of different Mg alloys have been inves-tigated both in vitro and in vivo, little work has been performed to character-ize the relationship between the com-position of Mg alloys, their corrosion and resulting mechanical properties over time. Consequently it is crucial to understand how these properties may be related if alloys are to be success-fully screened for implantation in the body.

intRoduction

Considerable research remains be-fore Mg alloys may be accurately screened and used in vivo. Whilst much in vitro research has occurred in this fi eld in the past half-decade, sec-ond order effects such as biocorrosion and its infl uence on related properties (i.e., mechanical strength) of Mg alloys require study particularly for ortho-pedic (load bearing) applications and when attempting to model implant de-sign lives. Calcium (Ca) is one of the most promising and biocompatible alloying elements for Mg. It is known to be the most abundant mineral in the body1 and plays a crucial role in the formation of

bone.2 Although several limited stud-ies have investigated Mg-Ca alloys for biomedical applications,3–7 a system-atic and primary study of the role of Ca additions upon dissolution of Mg alloys in vitro is lacking. Zinc (Zn) has been commonly used in Mg alloying for biomedical purpos-es.8–13 The addition of Zn to Mg results in improved mechanical properties from grain refi nement,14 has displayed a reduced corrosion rate compared with pure Mg in Hank’s Balanced Salt Solu-tion,8,15 and corroded at a suitable rate in vivo.9 Given its general non-toxici-ty, wide use, and biomedical potential, further investigation of its properties is merited. The addition of Zn to Mg-Ca alloys has been shown to improve the me-chanical properties (e.g. hardness) sig-nifi cantly.16 A number of studies have investigated Mg-Ca-Zn alloys in vitro, in both cast17–20 and bulk metallic glass form.21 The results have been promis-ing, and some Mg-Ca-Zn alloys have been reported to corrode slower than pure Mg in vitro.17 Incorporating two of the most biocompatible elements also minimizes any chance of toxicity-related problems when placed in vivo. Consequently Mg-Ca-Zn presents an interesting alloy system for further examination, and the basis for in vitro tests herein (Table I). The use of a simulated body fl uid (SBF) with a similar composition to in vivo conditions combined with a con-trolled environment is critical in the evaluation of the bio-dissolution rate of alloys. The inclusion of proteins, such as albumin, in an SBF has been shown to dramatically affect the corro-sion properties of metallic biomateri-als.26,27 Recently this has been further confi rmed in a range of tests on pure

Vol. 63 No. 6 • JOM 29www.tms.org/jom.html

Table I. Summary of Toxicity Limits Quoted in the Open Literature

for Elements Relevant to Mg-alloy Implant Materials22–25

ElementMaximum Daily Allowable

Dosage (mg)

Al 14

Be 0.01

Ca 1400

Ce 4.2

Cu 6

Fe 40

La 4.2*

Mg 400

Nd 4.2*

Ni 0.6

Pr 4.2*

RE 4.2*

Sn 3.5

Sr 5

Ti 0.8

Y 0.016*

Zn 15* Denotes that the total amount of these rare earth ele-ments (Ce, La, Nd, Pr, Y) combined should not exceed a value of 4.2 mg/day

Mg, as well as Mg alloyed with rare earth elements.28,29 As a consequence it is beneficial to utilize SBF supple-mented as closely as possible to the in vivo microenvironment that an implant will come into contact with. In this work, an in vitro corrosion (direct and electrochemical) study was coupled with mechanical testing to map the property space for Mg-Ca-Zn alloys. A ternary system is used as a model with the aim of tuning corrosion rates and strength for the purpose of developing customized resorptive implant alloys. Data management via a neural network model was shown to be of great value in terms of development of a holistic predictive tool. Additionally, it is also important to look at alloy performance over time, as some alloys may display deterioration of mechanical perfor-mance faster than others for the same level of dissolution/mass loss.

Results

Mechanical Performance of Mg-Ca-Zn Alloys

The results of compression test-ing (Figure 1) show that alloying

gives a wide spread of compressive strengths—with the data plotted in descending order. The change in com-pressive strength with immersion time in SBF shows a reduction in strength occurs with increasing immersion time (Figure 1). From Figure 1 we observe that for similar alloy loadings, Zn is more ef-fective than Ca in terms of increas-ing compressive strength. In regard to evaluated yield strength (sy), the results in Figure 2 present the data for un-corroded samples as a contour

plot. In addition, typical backscattered electron micrographs of select alloy compositions are also seen. We can see rather clearly that increased alloy load-ing leads to increases in yield strength, concomitant with a more heteroge-neous microstructure. At the highest alloy loadings, the volume fraction of intermetallic (expected to be MgZn2 or Mg2Ca) in such Mg-based alloys ap-proaches 50%.34

The yield strength of the alloys after three weeks immersion can be seen in Figure 3. When comparing with Figure 2,

No Immersion

1 Week in SBF

3 Weeks in SBF

450

400

350

300

250

200

150

100Max

imum

Com

pres

sive

Stre

ngth

(MPa

)

Mg-

20Zn

Mg-

10Zn

Mg-

16.2

Ca

Mg-

10C

aM

g-5C

a-6.

2Zn

Mg-

5Ca-

3Zn

Mg-

0.4C

a-6.

2Zn

Mg-

6.2Z

nM

g-1.

34C

a-6.

2Zn

Mg-

1.34

Ca-

3Zn

Mg-

5Ca

Mg-

3Zn

Mg-

1Zn

Mg-

0.4C

a-3Z

nM

g-0.

8Ca

Pure

Mg

Mg-

1.34

Ca

Mg-

0.4C

a

Figure 1. Ultimate compressive strength (UCS) of Mg alloys be-fore and after one and three weeks of immer-sion in MEM+BSA.

a b

d

ca

b

c

d

0 2 4 6 8 10 12 14 16Ca (wt.%)

20

18

16

14

12

10

8

6

4

2

0

Zn (w

t.%)

605550454035302520

(MPa)σy

Figure 2. Yield strength of Mg alloys before immersion with electron micrographs of se-lected areas.

JOM • June 201130 www.tms.org/jom.html

Table A. Elemental Composition of Investigated Alloys

Alloy Designation Ca Zn

Pure Mg < 0.002 < 0.005

Mg-0.4Ca 0.42 —

Mg-0.8Ca 0.84 —

Mg-1.34Ca 1.5 —

Mg-5Ca 4.55 —

Mg-10Ca 10.2 —

Mg-16.2Ca 16.2 —

Mg-28Ca 28 —

Mg-1Zn — 0.99

Mg-3Zn — 2.87

Mg-6.2Zn — 5.96

Mg-10Zn — 9.68

Mg-20Zn — 20.03

Mg-0.4Ca-3Zn 0.41 2.95

Mg-0.4Ca-6.2Zn 0.42 6.87

Mg-1.34Ca-3Zn 1.25 3.12

Mg-1.34Ca-6.2Zn 1.38 6.2

Mg-5Ca-3Zn 5.31 2.99

Mg-5Ca-6.2Zn 5.17 6.15

Values reported in wt.%Impurity levels were <150 ppm for all alloys

expeRimental pRoceduRes

Material Preparation

The alloys tested in this study were produced in-house. The bulk Mg and alloying ele-ments were placed in a mild steel crucible of 60 mm diameter and a height of 140 mm. The crucible was kept dry in an oven at 120ºC to minimize moisture build-up. Any mate-rial that came into contact with the molten alloy, including the crucible, stirrer, thermo-couple protector and mold, were coated with an alcohol-based graphite spray to minimize impurity pickup. The crucible was then placed into the furnace chamber and melting was performed in a purpose-built 10 kW induction furnace (Induktio GmBH, Ljubljana, Slovenia). The furnace chamber was evacuated to approximately 0.01 bar then filled with 0-Grade argon to create an inert environment and prevent the ignition of molten Mg that occurs in atmospheric conditions. The alloy was ramp-heated at approximately 3–5ºC/s to 350ºC, 500ºC, and then was held for 15–20 min. at 700ºC whilst stirring vigorously. The alloy was then poured into a rectangular mold that had been preheated to 400ºC using cartridge heaters. The measured cooling rate of the mold was ~1ºC/s. High-purity pure Mg was used in the production of alloys (99.99%, Timmenco Ltd., Toronto, Canada). Alloying additions were made via a master alloy of Mg-28Ca (Tim-menco), and the use of high-purity Zn (99.99%). Castings were prepared to have a final weight of 250 g. The chemical composition of all Mg alloys was tested independently via inductively coupled plasma atomic emission spectroscopy mass spectroscopy (ICP-AES, Spectrome-ter Services, Coburg, VIC, Australia). A list of all alloys tested and their composition is given in Table A. After casting, cylindrical samples were prepared with a diameter of 6 mm and a height of 12 mm using a lathe. To main-tain two parallel surfaces for compression tests, the ends of the cylinders were coated with epoxy to stop corrosion. A separate piece (10 mm × 20 mm × 20 mm) of each also was also prepared for electrochemical tests. Samples were then nominally ground with 240, 600, and 1,200 grit SiC paper to ensure consistency across all tests. High-purity ethanol (99.5%) was used to clean the samples between each polishing grade. Samples were then stored in a desiccator immediately after cleaning.

Immersion Tests

Mass loss tests were carried out over one and three weeks for all alloys investi-gated. The samples were placed into vented containers containing 20 mL of Minimum Essential Medium with 40 g/L of bovine serum albumin (MEM+BSA) per cm2 of surface area. This volume was chosen as it represented a ratio similar to the approxi-mate amount of blood plasma the body con-tains (2.75 L) relative to the approximate surface area of typical bone fixation de-vices (140 cm2).30 45% of the solution was replaced each day, based on daily urinary

it is apparent that there is a significant reduction in mechanical performance for all alloys, however the high Ca containing alloys are most affected. Images in Figure 3 indicate just how severely some of the alloys have cor-roded.

Corrosion Performance of Mg-Ca-Zn Alloys

When analyzing the overall mass loss rates of the different Mg alloys, it can be seen that increasing levels of Ca have a more detrimental effect than for similar levels of Zn (Figure 4). The Ca alloys have reached very high rates by 6 wt.%, whereas the mass loss rate for zinc-containing alloys plateaus at 9 wt.%. The electrochemical response of the alloys as determined through po-tentiodynamic polarization displays a similar trend, with higher current densities (and thus corrosion rates) for equivalent amounts of Ca versus Zn (Figure 5). Up to approximately 11 wt.% Zn there is little increase in the current density, indicating that Zn at these amounts is not detrimental to the corrosion. To describe the difference between the impact of Zn vs. Ca, an iso-concentration line is overlaid as an aid to the eye, showing how the rate of increase in icorr is much greater for Ca additives. Polarization curves of the individ-ual alloys indicated different electro-chemical responses due to increasing amounts of alloying element. As the Ca content increased, it can be readily seen that the corrosion potential be-comes more negative with a concomi-tant increase in corrosion rate (Figure 6). As such the icorr for Mg-Ca alloys are primarily anodically controlled. For the Mg-Zn alloys it was found that the corrosion potential becomes more positive with the increasing Zn content (Figure 7). This trend appeared to be primarily cathodically controlled, re-sulting in a more positive corrosion potential.

discussion

Calcium and Zn Alloying on the Performance of Mg Alloys Although the addition of Ca to pure Mg at levels above its solid solubil-ity (Cs, 1.34 wt.%) has been found

to increase the ultimate compressive strength by up to 1.5× (Figure 1), this benefit is significantly reduced by the concomitant increase in corrosion rates determined both through mass loss (Figure 4) and electrochemically (Fig-ure 5). Levels up to the Cs do not appear

to have a negative effect on the corro-sion, however they also do not provide any notable improvement in mechani-cal performance. Consequently it ap-pears that Ca, when the sole alloying element, offers little benefit over pure Mg, primarily due to the rapid dissolu-

Vol. 63 No. 6 • JOM 31www.tms.org/jom.html

excretion (1.5 L) versus the total volume of human blood plasma.28 These approxima-tions do not exactly replicate the actual environment a specific implant would experience in the body although the above assumptions help to justify the selected solution to surface area ratio. The MEM+BSA was buffered with either 2.2 g/L of sodium bicarbonate and placed in a CO2 incubator (Belco Glass, Vineland, NJ, USA) at 37ºC with a temperature vari-ance of 0.5ºC. This is standard practice for cell cultures using this buffer to maintain a physiological pH.31 All solutions were vacuum-assisted filtered using a 0.22 µm filter (Millipore Express PLUS Stericup®, Millipore Inc., Billerica, MA, USA). The pH was adjusted prior to testing to ensure the desired value (7.4) was achieved after steriliza-tion. During all testing, the pH was regulated to the desired value by adding controlled amounts of 1 M NaOH or 1 M HCl solutions. All filtering and sterilization was performed in a Clemco CF631 HEPA-filtered laminar flow hood (Clemco Ultraviolet, Artarmon, NSW, Australia). The mass of each sample was measured using an XP105 Analytical Balance (Mettler-Toledo Inc., Columbus, OH, USA) with an accuracy of 0.001 g. After removal from the solution, the corrosion products were dissolved by immersing the sample in a 2 M chromic acid solution (200 g/L CrO3, 10g/L AgNO3) for 10 min. at 40ºC. This removal technique is commonly employed in magnesium corrosion studies.32

Electrochemical Tests

Electrochemical tests were performed using a three electrode flat-cell (K0235, Princ-eton Applied Research, Oak Ridge, TN, USA). The cell contained 300 mL of media and had an exposed working electrode area of 1 cm2, and a saturated calomel (SCE) reference electrode and Ti-mesh counter electrode. Due to the difficulty of performing electro-chemical experiments in an incubator, 5.96 g/L of 2-(4-(2-hydroxyethyl)-1-piperazinyl) ethanesulfonic acid (HEPES, H3375, Sigma Aldrich Co., Auckland, New Zealand), a chemical buffer, was used. HEPES is commonly employed to buffer cell culture media in air and is widely used in in-vitro Mg experiments.28 Temperature was kept at 37 ± 0.5ºC using a silicone tube wrap jacket. Electrochemical experiments were performed on a BioLogic® potentiostat/galvanostat using EC-Lab 10.02 software (BioLogic Inc., Knoxville, TN, USA). Potentiodynamic polarization (PDP) experiments were carried out after 15 min. of open circuit (OCP) conditioning, which is similar to values used in other studies.33 Po-tentiodynamic scans were performed at a rate of 1 mV/s. Each test was carried out from –150 mV below the OCP to 500 mV above. The maximum current was limited to 2 mA. Values were recorded at 2 Hz for the entire test. A minimum of 5 separate scans were performed for each data point to ensure reproducibility.

Compression Tests

Mechanical compression tests were performed on the cylindrical samples before any exposure to SBF and after one and three weeks immersion. Tests were carried out on an Instron 4505 Testing System (Instron, Norwood, MA, USA). A 100 kN load cell was used, and the initial strain rate was set to 10–3 s–1. Flat platens were used and samples were prepared according to guidelines in ASTM E9-89a (2000). Due to the small sample size crosshead displacement was used to calculate the strain. Data was recorded at a rate of 100 Hz using Instron BlueHill 3.0 software.

Artificial Neural Network Creation

An artificial neural network (ANN) was created for both corrosion current density (icorr) and yield strength (sy) using JMP 8 statistical software (JMP 8, SAS, Cary, NC, USA). Prior to the creation of the network, three datasets were set aside to allow for final independent testing of the resulting ANN. Of the remaining datasets, 66% were used for training/creation of the ANN for icorr and sy, respectively. The remainder, 34% was used for cross-validation.

tion rates at levels above its Cs. Alloying with Zn (≥ 3wt.%) result-ed in a significant increase in both the UCS (25–200%) and the yield strength (50–80%) before immersion (Figures 1 and 2). However, at concentrations of 6.2 wt.% and above this improve-

ment in mechanical properties quickly eroded after immersion in MEM+BSA (Figure 3). Nonetheless, for equiva-lent levels of Ca, Zn alloys displayed greater mechanical and corrosion per-formance (Figure 5). Overall, Zn-rich Mg alloys do display potential for

further investigation, primarily due to their large improvement in mechanical property combined with only a margin-al increase in corrosion rate. Alloys containing Ca and Zn dis-played improvements in both UCS and yield strength when compared with pure Mg before immersion. However, most alloys exhibited substantial varia-tion in mechanical properties after both one and three weeks immersion (Fig-ure 1). By tailoring the amount of Ca and Zn, it may be possible to obtain an “ideal alloy” for a specific situation. It is evident at higher alloy loadings (par-ticularly >5 wt.%) the loss in mechani-cal properties is significant following 3 weeks immersion. We note that the mechanical prop-erties of the investigated alloys are closely linked to their corrosion per-formance. Although an alloy may ini-tially display favorable mechanical properties, its corrosion can result in severe variation or reduction in these properties over time. This is shown for Mg-5Ca-6.2Zn in Figure 1, where its high UCS before immersion de-creases significantly over three weeks. However, alloys such as Mg-3Zn and Mg-1.34Ca-3Zn displayed higher UCS and greater yield strength at compa-rable levels before and after immer-sion. Thus, basing alloy choice solely on data obtained before corrosion is flawed and should be avoided. The mechanistic reasons for the in-creased corrosion rates found in the al-loying systems investigated are shown in analysis of the polarization curves. Ca has been found to escalate the cur-rent density primarily though increas-ing the anodic reaction rates (Figure 6). In contrast, the corresponding cathodic branches are relatively unaffected by Ca additions. If one were to extrapolate the cathodic branch of the polariza-tion in Figure 6 for Mg-0.8Ca towards increasingly negative potentials, the extrapolated curve would overlap the curves of the high Ca containing al-loys. This is a unique property of the secondary Mg2Ca phase that has been discussed previously by Kirkland et al.34

Increasing levels of Zn has a dif-ferent effect, controlling the cathodic reaction rate, with faster kinetics ob-tained at higher Zn levels (Figure

JOM • June 201132 www.tms.org/jom.html

Figure 3. Yield strength of Mg alloys after three weeks immer-sion in MEM+BSA with images of selected alloys.

a b c

b

ac

d d

0 2 4 6 8 10 12 14 16Ca (wt.%)

20

18

16

14

12

10

8

6

4

2

0

Zn (w

t.%)

(MPa)σy

605550454035302520

Figure 4. Overall corrosion of Mg alloys in MEM+FBS ex-pressed as mg/cm2/day.

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16Ca (wt.%)

20

18

16

14

12

10

8

6

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0

Zn (w

t.%)

Mass loss mg/cm2/day

4.43.83.22.51.91.30.60.0

0 2 4 6 8 10 12 14 16Ca (wt.%)

20

18

16

14

12

10

8

6

4

2

0

Zn (w

t.%)

36032529025522018515011580

icorr

(mA/cm2)

Figure 5. Corrosion current density of Mg alloys investigated in MEM+FBS.

Figure 6. Polarization curves for selected Mg-Ca alloys with anodic trend indicated.

10-6 10-5 10-4 10-3

i (A/cm2)

-1.5

-1.6

-1.7

-1.8

-1.9

-2.0

E (V

SCE)

Mg-0.8CaMg-1.34CaMg-16.2CaMg-28Ca

Ca

7). Interestingly the Zn content itself causes a cathodic shift even below its Cs in Mg, which is furthered once this limit is reached. The anodic branches appear unaffected by the increased Zn content. Extrapolating the anodic branch of the Mg-xZn alloys towards increasingly positive potentials would eventually cause them to overlap, al-beit at relatively high currents. Thus greater electrochemical corrosion rates are found for both alloying systems, al-though the magnitude of the increased dissolution is significantly higher for the Mg-Ca system. Both the corrosion and mechanical performance of Mg alloys are critical to their success or failure when im-planted in vivo. In order to fully ap-preciate the relationship between the

alloying additions and these properties, a neural network may be created.

Corrosion-Mechanical Performance Relationship for Mg Alloys

Proper analytical interpretation of ternary (Mg, Ca, and Zn) data is chal-lenging due to the difficult nature of connecting the resultant properties to the original inputs. In order to manage the data in a holistic manner, it is ben-eficial to create a framework which al-lows prediction of performance based on a validated equation or system. An ANN model would allow the deter-mination of a property, such as icorr or yield strength, as a function of the Ca and Zn addition. This allows the user to identify the key factors that affect this

property, helping determine the most important mechanistic factors. Similar work has been previously reported by Cavanaugh et al.35,36

An ANN was developed for the Mg-Ca-Zn system investigated to predict both icorr and yield strength (Figure 8). An architecture of 4-hidden nodes was selected based on the R2 value of the model validation and to capture the process with appropriate fidelity. The results for the trained ANNs are shown in Figure 9. The R2 values, indicative of the accuracy of the fit, were 0.94 and 0.89 for training and validation, respec-tively. Although lower than unity, the correlation is relatively good when the complexity of the underlying processes (microstructure driven) that determines the material properties, and the rather

Vol. 63 No. 6 • JOM 33www.tms.org/jom.html

limited amount of data used to con-struct the model. What is clear is that much more data would be beneficial in the future, to create a model of better predictive fidelity and, in due course, to incorporate additional alloying ele-ments. In order to limit the size of this article, readers are referred to Refer-ence 36 and references therein for a more detailed description of the mod-eling approach used. The development, refinement, and use of such a predictive model is a key tool in the research towards optimized alloy development. Multi-property models allow variation of inputs and

50 100 150 200 250 300 350 400icorr predicted (mA/cm2)

400

350

300

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100

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i corr (m

A/cm

2 )

20 25 30 35 40 45 50 55 60

predicted (MPa)

σ y

σy

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(MPa

)

the resultant output to be predicted, yielding a simple design tool. Neces-sarily the mechanistic aspects must be studied in dedicated tests (i.e. electron microscopy and polarization testing). The influence of Ca and Zn on the mechanical and corrosion per-formance found in this study may be seen interactively at the site created by the authors (http://users.monash.edu.au/~nickbir/MgCaZn.swf). This allows the user to vary the amount of alloying element and see the effect on icorr and σy in real-time. It is also an example of how ANNs and predicted performance may better help researchers more read-

10-6 10-5 10-4 10-3

i (A/cm2)

-1.5

-1.6

-1.7

-1.8

-1.9

-2.0

E (V

SCE)

Mg-1ZnMg-3ZnMg-6.2ZnMg-10ZnMg-20Zn

ZnFigure 7. Polarization curves for Mg-Zn alloys with cathodic trend indi-cated.

Mg

Ca

Zn

H1

H2

H3

H4

icorrσ

y

Figure 8. ANN architecture consisting of three inputs, a hidden layer with four nodes, and the output, icorr and sy.

Figure 9. ANN-predict-ed versus observed (a) σy and (b) icorr values.

a b

ily understand the potential behavior of alloys they create. For the example given, users are cautioned to limit the alloying amounts to positive values and to a total alloy loading (Ca+Zn) of <26 wt.% in order to remain within the predictive ability of the model. In addition to the above, whilst it will not be discussed here, other stra-tegic elements can also contribute to wider ranges of property customization and greater fidelity in the achievement of specific properties. For example traces of zirconium can refine grain size, leading to increases in strength (exploiting the Hall–Petch effect) and with little penalty on corrosion. Ele-ments with wide solid solubilities can also be used to give a fine-tuning of reaction rates with little impact on the microstructure.

conclusions

The in vitro corrosion and mechani-cal performance is dictated by both the chemistry and microstructures devel-oped in the family of Mg-Ca-Zn alloys. Corrosion/dissolution is intensified with increasing strength, meaning there is a trade-off between these properties. However, the ratio of Zn to Ca can be modified to give a balance of strength and dissolution rate—resulting in customized performance. This is pos-sible owing to the fact that Ca and Zn give different strengthening responses (which can be exploited) and that Ca and Zn dictate corrosion kinetics by completely different mechanisms (an-odic vs. cathodic control). Bundling of this into a property-predictive tool was possible via the use of simple neural network models. We foresee the op-

JOM • June 201134 www.tms.org/jom.html

To read more about them, turn to page 10. To join TMS, visit www.tms.org/Society/Membership.aspx.

Nicholas T. Kirkland and Nick Birbilis are TMS Members!

portunity to engineer a customized dis-solution rate over a range of Mg-based alloys by expanding the portfolio of al-loys studied.

acKnoWledGements

The ARC Centre of Excellence for Design in Light Metals and the Monash MRA scheme. Dr. M.K. Cavanaugh is acknowledged for her assistance with ANN models.

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Nicholas T. Kirkland, Research Associate, Chris H.J. Davies, Professor, and Nick Birbilis, Associ-ate Professor, are with the Department of Materi-als Engineering, Monash University, Clayton, 3800, Victoria, Australia; Mark P. Staiger, Senior Lecturer, and Kirkland (jointly) are with BioMATE Group, Mechanical Engineering Department, University of Canterbury, Engineering Road, Ilam, Christchurch 8041, New Zealand; David Nisbet, Senior Research Fellow, is with the Research School of Engineer-ing, The Australian National University, Acton, 0200, Australian Capital Territory, Australia. Dr. Bir-bilis can be reached at (+61) 3-990-54919; e-mail nick. birbilis@monash.edu.