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Effect of refractory elements and Al on the high temperature oxidation of Ni-base superalloys and modelling of their oxidation resistance
Journal of Alloys and Compounds 710 (2017) 8e19
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Journal of Alloys and Compounds journal homepage: http://www.elsevier.com/locate/jalcom
Effect of refractory elements and Al on the high temperature oxidation of Ni-base superalloys and modelling of their oxidation resistance Dae Won Yun, Seong Moon Seo, Hi Won Jeong, Young Soo Yoo* Korea Institute of Materials Science, 797 Changwondaero, Changwon, Gyungnam 641-831, South Korea
a r t i c l e i n f o
a b s t r a c t
Article history: Received 23 November 2016 Received in revised form 20 February 2017 Accepted 17 March 2017 Available online 19 March 2017
The oxidation resistance of Ni-Co-Cr-Al-W-Mo-Ta-Re-Ru alloys is evaluated by cyclic oxidation at 1100 C and modelled using an artificial neural network. The database required for the modelling was constructed using design of experiment (the Box-Behnken method) followed by oxidation experiments. The obtained model with a 7-10-1 architecture exhibits consistent prediction of the experimental data (R ¼ 0.999). Cr and Al enhance the oxidation resistance by promoting the formation of a protective NiAl2O4 layer. Mo and Ru are detrimental to the oxidation resistance. W, Ta and Re exhibit complex behaviours depending on the contents of other alloying elements. © 2017 Elsevier B.V. All rights reserved.
Keywords: High-temperature alloys Superalloys Nickel Oxidation Modelling SEM
1. Introduction Gas turbines are key systems in both the aerospace and thermal power plant industries. Since gas turbine components are exposed to high mechanical loading and severe environmental attack at high temperatures, Ni-base superalloys are widely used in gas turbine engines. Increasing the turbine inlet temperature results in a favourable reduction in the fuel cost and emission by enhancing the efficiency of gas turbine engines [1]. However, mechanical properties, such as the high temperature tensile strength and creep strength, and the environmental resistance, such as the oxidation resistance of materials for turbine components, should be further improved to increase the operating temperature. To enhance the high temperature tensile strength and creep strength of Ni-base superalloys, the content of refractory metals such as Mo, W, Ta, Re and Ru has been increased, and the content of Cr, which provides oxidation resistance, has been decreased. Although these compositional changes improve the mechanical properties [2e5], they can be detrimental to the oxidation resistance [6e8]. In particular, Ru, Mo and Re can form volatile oxide species; therefore, the oxidation resistance of alloys containing a large amount of these elements can
* Corresponding author. E-mail address: [email protected] (Y.S. Yoo). http://dx.doi.org/10.1016/j.jallcom.2017.03.179 0925-8388/© 2017 Elsevier B.V. All rights reserved.
be deteriorated [9e12]. Since both the mechanical and oxidation properties are important in superalloys, it is important to balance both properties in the development of superalloys. For a large number of alloying elements used in superalloys, computational modelling can be an effective tool in the development of superalloys with balanced mechanical and oxidation properties [13e16]. In a previous study on Ni-Cr-W-Mo alloys, artificial neural networks (ANN) were successfully employed to model the oxidation properties [16]. However, the growing number of variables exponentially increases the number of experiment required to generate the database for the modelling. For a full factorial design with 7 variables and 3 levels, 2187 samples have to be tested, which is practically impossible. Therefore, design of experiments (DOE) can solve this problem by reducing the number of experiments required to obtain relations between the variables and properties. By combining computational modelling with DOE, a promising tool can be created for the development of superalloys. In this study, 6 refractory elements (Cr, Mo, W, Ta, Re and Ru) and Al were selected as variables with a compositional range of Ni6Co-(2e8)Cr-(0e3)Mo-(2e10)W-(4.5e6.5)Al-(2e10)Ta-(0e6)Re(0e6)Ru in wt%. If a full factorial design were employed with 3 levels in each component, 2187 samples would be tested, as mentioned earlier. By using the Box-Behnken method, the number of samples required to generate the database for ANN modelling is reduced to 62 samples with 57 different compositions. In the Box-
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Behnken design for 7 factors, 6 samples with identical chemical compositions with the centre point of the compositional range (Ni6Co-5Cr-1.5Mo-6W-5.5Al-6Ta-3Re-3Ru) should be tested due to the statistical importance. The oxidation resistance of the samples was evaluated by a cyclic oxidation test for 50 cycles with a one-hour dwell at 1100 C. The database of the weight changes after 50 cycles was constructed and modelled by using ANNs, which is similar to a previous study [16]. The model was successfully constructed in the Cr-Mo-W-AlTa-Re-Ru space and was consistent with the experimental data (with multiple correlation coefficient R ¼ 0.999). The weight change behaviour was analysed using a simple statistical spalling model proposed by Poquillon and Monceau [17]. The oxide microstructure was analysed with scanning electron microscopy (SEM) to study the effect of each element on the high temperature oxidation.
samples are prepared by electrical discharge cutting and microcutting to make discs with 10 mm diameters and 3 mm thicknesses. The samples were ground using SiC papers up to 1000-grit and cleaned in acetone and ethanol before the cyclic oxidation experiments. Cyclic oxidation is performed in an open-end furnace equipped with a programmable specimen in/out system. One cycle of experiments in this study is composed of heating at 1100 C for 60 min and cooling in open air for 30 min to room temperature. Fig. 1 shows the actual temperature profile during the cyclic oxidation experiment. The weights of samples are measured after 0, 2, 4, 16, 20, 32, 36 and 50 cycles using a digital balance with an accuracy of 104 g. Spalled oxide scales were not incorporated in the weight measurement. After the oxidation experiment, the oxide scale of the specimens was examined by SEM in the backscattered electron imaging (BEI) mode and energy dispersive spectroscopy.
2. Materials and methods
3. Results and discussion
In the alloy design framework, the goal was to find out the effect of alloying elements including Cr, Mo, W, Al, Ta, Re, and Ru on the oxidation resistance of Ni-base superalloy. The Box-Behnken design was employed to DOE to minimize the number of samples in this study. This design scheme requires smaller experiment runs than the other schemes such as central composite design (CCD); the CCD gives 152 runs for 7 independent variables while the Box-Behnken design [18] gives 62 runs for the same number of variables. In Box-Behnken design, the levels of the concentrations of alloying elements were determined according to three coded levels designated as 1, 0, and þ1. These codes represent the lower limit, the central value, and the upper limit of an independent variable, respectively. The zero level is presented as the central design point, corresponding to the central value of the range of independent variables under interest. The central points were repeated for 6 times to stabilize the extrapolation around the central region. The 1 and þ1 levels are edge points which correspond to the maximum and the minimum values to be tested. The compositional ranges of tested alloys are shown along with the coded levels in Table 1. The lower limit and the upper limit of each factor represent reasonable range of alloying elements in single crystal Ni-base superalloys. 62 alloy compositions were designed according to the Box-Behnken method for 7 alloying elements (Table 2). The alloys from A57 to A62 have an identical composition as a centre point of the compositional range. To save space, the weight changes after 50 cycles, which are used in ANN modelling, are also listed in Table 2. The samples were prepared by vacuum arc melting. Impurities that are detrimental to the oxidation resistance, such as S, are strictly controlled during the raw material selection and the melting process under 1 ppm. From each ingot, the oxidation
Table 1 Level values used in the Box-Behnken method. Level
Cr Al W Mo Ta Re Ru Co Ni
1
0
þ1
2 4.5 2 0 2 0 0 e e
5 5.5 6 1.5 6 3 3 6 Bal.
8 6.5 10 3 10 6 6 e e
3.1. Weight changes during cyclic oxidation The weight change of selected experimental alloys during the cyclic oxidation is shown in Fig. 2. One thermal cycle consisted of exposure at 1100 C for 1 h and cooling for 30 min to room temperature. The alloys shown in Fig. 2 contain 5 wt% Cr except A47 and A48. The alloys with 8 wt% Cr or 5 wt% Cr þ 6.5 wt% Al, including the alloys that are not presented in Fig. 2, showed no significant weight gain or loss during the experiment, which indicates excellent oxidation resistance. The oxidation resistance of these alloys were better than that of CMSX-4, which is known to have excellent oxidation resistance among single crystal Ni-base superalloys, in the literature [19]. The other alloys showed significant amounts of weight loss. In general, the addition of Cr and Al was beneficial to the oxidation resistance. The addition of Ta was slightly beneficial in some cases. Mo, W and Ru were detrimental to the oxidation resistance as known in the literature [6e8]. Re had no significant effect on the oxidation resistance. For an in-depth study of the effect of alloying elements, the weight change after 50 cycles of the oxidation experiment with respect to the content of each alloying element is plotted in Fig. 3. An increase in the Cr content from 2 wt% to 8 wt % significantly improved the oxidation resistance (Fig. 3(a)). An increase in the Al content from 4.5 wt% to 6.5 wt% also improved the oxidation resistance, except for the alloys with 8 wt% Cr (Fig. 3(b)). Mo addition up to 3 wt% was detrimental to the oxidation resistance. However, its detrimental effect was reduced when the content of W was 2 wt% (Fig. 3(c)). The effect of W on the oxidation resistance was generally negative. However, its negative effect was diminished when the contents of Cr and Al were sufficiently high, as in the case of the alloys with 8 wt% Cr and 5 wt% Cr þ 6.5 wt% Al (Fig. 3(d)). The addition of Ta was somewhat beneficial up to 10 wt %, except for the alloys with 2 wt% Cr or 8 wt% Cr (Fig. 3(e)). For the alloys with 2 wt% Cr, the addition of Ta showed a negative effect. Moreover, no significant effect was observed for alloys with 8 wt% Cr. Both positive and negative effects of Ta on the oxidation resistance of Ni-base alloys were reported in the literature [20,21]. The effect of Re on the oxidation resistance was not monotonous, and it differed depending on the content of Cr and Al (Fig. 3(f)). Moniruzzaman et al. reported that the harmful effect of Re was altered by the content of Al [22], which is similar results with this study. The addition of up to 6 wt % Ru resulted in an adverse effect on the oxidation resistance. Similar to the effect of W, the adverse effect of Ru was reduced when the contents of Cr and Al were sufficiently high (Fig. 3(g)).
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Table 2 Nominal alloy composition used in this study and their oxidation properties. The compositions are expressed in weight percent (wt%). Alloy Name
Cr
Mo
W
Al
Ta
Re
Ru
Co
Ni
Weight change after 50 cycles (mg/cm2)
A01 A02 A03 A04 A05 A06 A07 A08 A09 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 A27 A28 A29 A30 A31 A32 A33 A34 A35 A36 A37 A38 A39 A40 A41 A42 A43 A44 A45 A46 A47 A48 A49 A50 A51 A52 A53 A54 A55 A56 A57 A58 A59 A60 A61 A62
5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 2.0 8.0 2.0 8.0 2.0 8.0 2.0 8.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 2.0 8.0 2.0 8.0 2.0 8.0 2.0 8.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 2.0 8.0 2.0 8.0 2.0 8.0 2.0 8.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0
1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 0.0 3.0 0.0 3.0 0.0 3.0 0.0 3.0 0.0 0.0 3.0 3.0 0.0 0.0 3.0 3.0 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 0.0 3.0 0.0 3.0 0.0 3.0 0.0 3.0 1.5 1.5 1.5 1.5 1.5 1.5
6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 2.0 10.0 2.0 10.0 2.0 10.0 2.0 10.0 2.0 2.0 10.0 10.0 2.0 2.0 10.0 10.0 2.0 2.0 10.0 10.0 2.0 2.0 10.0 10.0 6.0 6.0 6.0 6.0 6.0 6.0
4.5 6.5 4.5 6.5 4.5 6.5 4.5 6.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 4.5 4.5 4.5 4.5 6.5 6.5 6.5 6.5 4.5 4.5 6.5 6.5 4.5 4.5 6.5 6.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5
2.0 2.0 10.0 10.0 2.0 2.0 10.0 10.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 2.0 2.0 10.0 10.0 2.0 2.0 10.0 10.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 2.0 2.0 2.0 2.0 10.0 10.0 10.0 10.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0
0.0 0.0 0.0 0.0 6.0 6.0 6.0 6.0 0.0 0.0 6.0 6.0 0.0 0.0 6.0 6.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 0.0 0.0 0.0 0.0 6.0 6.0 6.0 6.0 3.0 3.0 3.0 3.0 3.0 3.0
3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 0.0 0.0 0.0 0.0 6.0 6.0 6.0 6.0 0.0 0.0 0.0 0.0 6.0 6.0 6.0 6.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 0.0 0.0 0.0 0.0 6.0 6.0 6.0 6.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0
6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0
bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal.
39.6 6.8 18.7 3.5 28.0 14.4 27.3 0.0 42.4 3.3 28.7 2.3 66.6 3.2 59.2 3.8 8.1 18.8 4.0 10.6 17.2 32.0 13.7 20.1 85.5 2.0 71.8 3.1 18.5 7.9 34.1 2.0 21.5 30.7 12.3 7.9 36.3 49.6 12.1 8.2 22.0 8.2 56.3 3.5 64.3 2.3 42.2 0.0 10.7 16.0 9.2 21.7 10.9 14.1 16.6 21.5 18.0 21.7 20.5 17.5 22.2 20.5
Prior to ANN analysis, linear model analysis was also performed with the weight changes of the alloys with 5 wt% Cr and 5.5 wt% Al after the cyclic oxidation to investigate the effect of each refractory element on the oxidation resistance; the result is presented in Fig. 4. The output value was calculated by the following equation. The linear coefficients and the constant were determined to minimize the sum of squared deviations.
yðxÞ ¼
I X
wi xi þ c
(1)
i¼1
xi is the weight percent of each alloying element as the input value, y(x) is the weight change after 50 cycles as the output value, wi and c are the linear coefficient for each alloying element and the constant for the model.
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spallation. Mo and Ru deteriorated the oxidation resistance by increasing spallation. Ta showed a beneficial effect on reducing spallation. Furthermore, the addition of up to 6 wt% Re was negligible to the oxidation resistance. The effect of W could not be analysed by the model due to the incompatibility of the fitted curves by the model with the experimental data. 3.2. Microstructure of the oxide scale
Fig. 1. The actual temperature profile during the cyclic oxidation experiment.
Fig. 2. Weight change with oxidation cycles of one-hour exposures at 1100 C for selected experimental alloys and CMSX-4 from the literature [19].
The other composition range of Cr and Al was not analysed, as their data points were not sufficient for the analysis. The calculated value from the linear model showed a reasonable agreement with the experimental data (Fig. 4(a)). Mo and Ru were detrimental to the oxidation resistance, and W also showed a negative effect. Re only had a small negative effect on the oxidation resistance, and Ta was beneficial to the oxidation resistance in a given composition range (Fig. 4(b)). To separately observe the effect of alloying elements on the spallation of an oxide scale and the oxidation rate, the weight change data were analysed using a simple statistical model [17]. Some assumptions in the model are not valid in this study. However, the spalling probability and the parabolic rate constant obtained by the model can be useful for a study that compares the cyclic oxidation of alloys with similar chemical compositions. Moreover, the model was successful in our previous study for analysing the effect of Si and Al on the oxidation resistance of Ni-CrW-Mo alloys [23]. The fitted lines in Fig. 2 show the plausibility of the model. The spalling probability and the parabolic rate constant of selected experimental alloys obtained by the model are presented in Fig. 5. Cr was effective in reducing both spallation and the oxidation rate, whereas Al was mainly effective in reducing
After the cyclic oxidation experiment, the oxide scale of the specimens was examined by SEM. To observe the effect of the alloying element on the microstructure of oxide scale, the oxide microstructure of selected specimens were compared. The micrographs show the representative microstructure of the oxide scale where the oxide scales was relatively intact. The areas where the oxide scale was spalled out were not shown, because they do not show the microstructure of the oxide scales properly. In the areas where the oxide scale was relatively intact, the microstructure of the oxide scale was quite uniform. Since the addition of up to 6 wt% Re had negligible effect on the oxide microstructure, micrographs that compare the oxide microstructure of specimens with and without Re are not presented. The oxide phases were mainly identified by energy dispersive spectroscopy (EDS) analysis. Analysis results of X-ray diffraction and EDS in previous studies with similar oxide microstructure [16,23] and analysis results in the literature [24e29] were taken into consideration when identifying the oxide phases. Fig. 6 shows the effect of Cr on the oxide microstructure. The typical oxide scale of specimen A41 with 2 wt% Cr consisted of a thick mixed oxide layer, a Ni-Cr spinel layer and a discrete NiAl2O4 layer. In addition, the mixed oxide layer was composed of NiO, NiCr spinels and NiWO4 particles, which appear as white particles (Fig. 6(a)). The outermost part of the oxide scale, which is presumably a NiO layer, is seemingly spalled during the cooling process. In contrast, the oxide scale of specimen A42 with 8 wt% Cr consisted of a thin mixed oxide layer, a Ni-Cr spinel layer and a continuous NiAl2O4 layer. The mixed oxide layer was mainly composed of Ni-Cr spinels, and it contains a small amount of NiO and NiWO4 particles (Fig. 6(b)). The increase in the Cr content suppressed the formation of the transient oxides of NiWO4 and NiO and promoted the formation of a protective NiAl2O4 layer by acting as an oxygen getter [30,31]. Fig. 7 shows the effect of Al on the oxide microstructure. The oxide scale of specimen A01 with 4.5 wt% Al mainly consisted of a NiO layer and a mixed oxide layer. The mixed layer was composed of either NiO-NiWO4 (Fig. 7(a)) or NiO-spinel-NiWO4 (Fig. 7(b)). The innermost part of the oxide scale consisted of either an internal oxidation zone of spinels (Fig. 7(a)) or a discrete NiAl2O4 layer (Fig. 7(b)). Cracks were observed along the mixed oxide layer, which aggravate spallation of the oxide scale. On the contrary, the oxide scale of specimen A02 with 6.5 wt% Al consisted of a mixed oxide layer, a Ni-Cr spinel layer and a continuous NiAl2O4 layer. Moreover, the mixed oxide layer was composed of NiO, Ni-Cr spinels and NiWO4 particles (Fig. 7(c)). A small amount of NiO was also found on the outermost part of the oxide scale. The increase in the Al content promoted the formation of a protective NiAl2O4 layer, and it also reduced the formation of the mixed oxide layer, which is prone to spallation. Fig. 8 shows the effect of Mo on the oxide microstructure. In both the specimens of A49 without any Mo and A50 with 3 wt% Mo, the oxide scales were composed of a NiO layer, a mixed oxide layer, a Ni-Cr spinel layer and a NiAl2O4 layer. Furthermore, the formation of oxide nodules was observed in some areas of both specimens (Fig. 8(a), (c) and (d)). However, the size of the oxide nodules formed on the specimen with 3 wt% Mo was much larger, and the
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Fig. 3. Weight change after 50 cycles of the oxidation experiment with respect to the content of (a) Cr, (b) Al, (c) Mo, (d) W, (e) Ta, (f) Re and (g) Ru.
D.W. Yun et al. / Journal of Alloys and Compounds 710 (2017) 8e19
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Fig. 4. Linear model analysis result for the weight changes after the cyclic oxidation of the alloys with 5 wt% Cr and 5.5 wt% Al: (a) experimental and calculated weight changes and (b) linear coefficient of each alloying element.
Fig. 5. Parabolic rate constant and spalling probability of selected experimental alloys calculated by the simple statistical spalling model.
nodules were more frequently observed. It seems that the evaporation of Mo species deteriorated the stability of the protective oxide scale. This promoted the formation of oxide nodules. A similar effect of Mo was found in a study with ferritic stainless steels [32]. Fig. 9 shows the effect of W on the oxide microstructure. In both specimens of A53 with 2 wt% W and A55 with 10 wt% W, the oxide scales consisted of a NiO layer, a mixed oxide layer, a Ni-Cr spinel layer and a continuous NiAl2O4 layer. However, thick layers of NiO and NiO-spinel-NiWO4 mixed oxides were found in several areas of specimen A55 with 10 wt% W (Fig. 9(c)). Since this mixed oxide layer contains a large amount of pores, it is prone to cracking and spalling (Fig. 9(d)). An increase in the W content from 2 wt% to 10 wt% facilitated the growth of NiO and the mixed oxide layer. Fig. 10 shows the effect of Ta on the oxide microstructure. The oxide scale of specimen A17 with 2 wt% Ta mainly consisted of a NiO layer, a mixed oxide layer, Ni-Cr spinels and a NiAl2O4 layer (Fig. 10(a)). The formation of a discrete Al2O3 layer was observed in only a limited area (Fig. 10(b)). In specimen A19 with 10 wt% Ta, the formation of NiO and NiWO4 was suppressed; moreover, CrTaO4 was found beneath or inside the NiAl2O4 layer (Fig. 10(c) and (d)).
Fig. 6. BEI cross-sectional microstructure of (a) specimen A41 with 2 wt% Cr and (b) specimen A42 with 8 wt% Cr after 50 cycles of cyclic oxidation.
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Fig. 7. BEI cross-sectional microstructure of (a) and (b) specimen A01 with 4.5 wt% Al and (c) specimen A02 with 6.5 wt% Al after 50 cycles of cyclic oxidation.
Fig. 8. BEI cross-sectional microstructure of (a) and (b) specimen A49 without Mo and (c) and (d) specimen A50 with 3 wt% Mo after 50 cycles of cyclic oxidation.
D.W. Yun et al. / Journal of Alloys and Compounds 710 (2017) 8e19
Fig. 9. BEI cross-sectional microstructure of (a) specimen A53 with 2 wt% W and (b), (c) and (d) specimen A55 with 10 wt% W after 50 cycles of cyclic oxidation.
Fig. 10. BEI cross-sectional microstructure of (a) and (b) specimen A17 with 2 wt% Ta and (c) and (d) specimen A19 with 10 wt% Ta after 50 cycles of cyclic oxidation.
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Fig. 11. BEI cross-sectional microstructure of (a) and (b) specimen A19 without Ru and (c) and (d) specimen A23 with 6 wt% Ru after 50 cycles of cyclic oxidation.
Fig. 12. Real and predicted values of the cyclic oxidation weight change. The prediction was made by the ANN model with a 7-10-1 architecture. Error bars are also provided.
The formation of CrTaO4 in Ni- and Co-base alloys with Ta and Cr was reported in the literature [26e29]. Another change in the oxide microstructure was the formation of a continuous Al2O3 layer.
Compared to specimen A17 with 2 wt% Ta, the formation of Al2O3 was more frequently observed in specimen A19 with 10 wt% Ta. Additionally, areas with a continuous Al2O3 layer, as shown in Fig. 10(d), were also found in specimen A19. Ta presumably acted as an oxygen getter similar to Cr, which promoted the formation of a continuous Al2O3 layer. Since the equilibrium oxygen partial pressure of Ta (8.3 1023 atm [33]) is slightly lower than that of Cr (2.6 1020 atm [34]) at 1100 C, Ta can also act as an oxygen getter for the formation of Al2O3. Fig. 11 shows the effect of Ru on the oxide microstructure. In specimen A19 without any Ru, the oxide scale mainly consisted of a Cr-Ni spinel layer and a NiAl2O4 layer, and NiO and NiWO4 were found in some areas (Fig. 11(a)). Moreover, the formation of NiO and NiWO4 was suppressed in the area with a CrTaO4 layer (Fig. 11(b)). However, a thick NiO layer and a mixed oxide layer, composed of NiO, Ni-Cr spinels and NiWO4, were formed on specimen A23 with 6 wt% Ru (Fig. 11(c)). The formation of NiWO4 particles was observed in even the area with a CrTaO4 layer (Fig. 11(d)). Similar to Mo, Ru seems to deteriorate the stability of the protective oxide scale and to promote the formation of NiO and NiWO4 by forming volatile oxide species. The changes in the oxide microstructure with respect to the changes in the content of each alloying element were consistent with the weight change data of Fig. 2. Cr and Al improved the oxidation resistance by promoting the formation of protective NiAl2O4 and by suppressing the formation of transient oxides such as NiO and NiWO4. Mo and Ru disturbed a barrier property of the oxide scale and promoted the formation of transient oxides by forming volatile oxide species such as MoO3 and RuO3 [9e12]. W also deteriorated the oxidation resistance by accelerating the
D.W. Yun et al. / Journal of Alloys and Compounds 710 (2017) 8e19
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Fig. 13. Contour maps of the weight change after 50 cycles in terms of the W and Mo contents for Ni-6Co-xCr-yAl-6Ta-3Re-3Ru alloys generated by the ANN prediction. A scale bar indicating the weight change value is located on the right side of each contour map.
formation of the mixed oxide layer containing NiWO4. Ta enhanced the oxidation resistance by encouraging the formation of a continuous Al2O3 layer and by suppressing the formation of transient oxides. The addition of up to 6 wt% Re had a negligible effect on the oxidation resistance and the microstructure. 3.3. Artificial neural network modelling The cyclic oxidation database is constructed by setting contents of Cr, Al, Mo, W, Ta, Re and Ru as the input parameters and the weight change after 50 cycles as the output (Table 2). A program by Radford Neal [35] was used in the ANN training and prediction in this study. The output value, y(x), is calculated by following equations.
yðxÞ ¼
H X
gh vh þ bo
(2)
h¼1
gh ðxÞ ¼ tanh
I X i¼1
! xi wih þ bh
(3)
Here, xi and gh(x) are the input value and the value of the hidden node, wih and bh are the weight and the bias on the connection from input i to hidden node h, vh and bo are the weight and the bias on the connection from hidden node h to output. The values of the weights and the biases are randomly assigned as an initial value. By neural network training process, the values of the weights and the biases are adjusted to predict the output value properly. Detailed information on ANN training can be found in previous studies [15,16]. Only one hidden layer is employed in this study, which can be expressed as ‘7-x-1’ with ‘x’ as the number of nodes in the hidden layer. A total of 46 architectures from ‘7-5-1’ to ‘7-50-1’ were tested to find a model consistent with the actual experimental data while preventing over-fitting [36,37]. Among the tested architectures, a 7-10-1 architecture showed the highest value of the multiple correlation coefficient R (R ¼ 0.999) and was chosen for the study. The real and predicted weight change data are shown in Fig. 12. The predicted value by ANN modelling shows good agreement with the experimental data. The short error bars in Fig. 12 also indicate that each prediction is performed with very high confidence. Figs. 13 and 14 show contour maps of the weight change after 50 cycles predicted by the ANN model, which are a part of the ANN
18
D.W. Yun et al. / Journal of Alloys and Compounds 710 (2017) 8e19
Fig. 14. Contour maps of the weight change after 50 cycles in terms of the Re and Ru contents for Ni-6Co-xCr-yAl-6Ta-6W-1.5Mo alloys generated by the ANN prediction. A scale bar indicating the weight change value is located on the right side of each contour map.
modelling results. The contour maps in Fig. 13 were plotted by changing the W content from 2 to 10 wt% and the Mo content from 0 to 3 wt% with a step size of 0.1 wt% for various Cr and Al contents. As observed in the scale bar of the contour maps, the overall weight loss was reduced as the Cr and Al contents increased. However, the effect of Mo and W varied with the Cr and Al contents. In alloys with 2 wt% Cr and 4.5 wt% Al, it was predicted that an increase in the W and Mo contents would result in a reduction in the weight loss. A similar behaviour was observed in a study with Ni-Cr-W-Mo alloys [16]. The reduced thermal expansion mismatch between a metal substrate and an oxide scale was proposed as a mechanism for the reduction in the weight loss of alloys with low Cr content and high W and Mo contents [16]. In alloys with more than 5 wt% Cr or 5.5 wt % Al, an increase in the W and Mo contents was generally harmful for the oxidation resistance. As observed from Fig. 3(c), the harmful effect of Mo was reduced with 2 wt% W in alloys with 5 wt% Cr and 5.5 wt% Al. In alloys with 5 wt% Cr þ 6.5 wt% Al and alloys with 8 wt % Cr, however, the harmful effect of W on the oxidation resistance was diminished. The contour maps in Fig. 14 were plotted by changing the Re content from 0 to 6 wt% and the Ru content from 0 to 6 wt% with a step size of 0.1 wt% for various Cr and Al contents. In the same
manner as Fig. 13, the overall weight loss was reduced as the Cr and Al contents increased. Ru was detrimental to the oxidation resistance in all cases. However, the effect of Re was varied depending on the Cr and Al contents. In alloys with 2 wt% Cr and 5 wt% Cr þ 4.5 wt% Al, it was predicted that the addition of up to 6 wt% Re would be slightly beneficial to the oxidation resistance. In alloys with 8 wt% Cr and 6.5 wt% Al, the addition of Re also had a beneficial effect. In contrast, the addition of Re was harmful to the oxidation resistance in alloys with 5 wt% Cr þ 6.5 wt% Al and 8 wt% Cr þ (4.5e5.5) wt% Al. In alloys with 5 wt% Cr and 5.5 wt% Al, Re only had a small amount of negative effect on the oxidation resistance. This is consistent with the result of the linear model analysis (Fig. 4). As observed from Figs. 13 and 14, the effect of an alloying element can be altered by the contents of other alloying elements. Since there is complex interaction between the alloying elements, in most cases, their behaviour cannot be fitted by a simple linear model. Moreover, modelling becomes more difficult as the number of variables increases. By using ANN, however, the oxidation resistance was successfully modelled in the complex system with 7 variables. This combined method of DOE and ANN can be a useful tool in the development of not only superalloys but also other
D.W. Yun et al. / Journal of Alloys and Compounds 710 (2017) 8e19
complex alloys. 4. Conclusions
[2] [3]
To model the oxidation resistance of Ni-base superalloys with a chemical composition range of Ni-6Co-(2e8)Cr-(0e3)Mo-(2e10) W-(4.5e6.5)Al-(2e10)Ta-(0e6)Re-(0e6)Ru with ANN, weight changes were measured after 50 cycles of a one-hour dwell at 1100 C. The Box-Behnken method was used to reduce the number of samples required to generate the database for the modelling. Compared to the full factorial design, the required number of samples reduced from 2187 to 62 by this method. The obtained ANN model had a 7-10-1 architecture and a multiple correlation coefficient of 0.999. An increase in Cr and Al was beneficial to the oxidation resistance by promoting the formation of a protective NiAl2O4 layer and by suppressing the formation of transient oxides such as NiO and NiWO4. An increase in Mo and W was generally harmful to the oxidation resistance by promoting the formation of NiO and a porous mixed oxide layer that consisted of NiWO4 and Ni-Cr spinel, except in the alloys with 2 wt% Cr and 4.5 wt% Al. In those alloys, an increase in Mo and W reduced the weight loss. In alloys with 5 wt% Cr þ 6.5 wt% Al and alloys with 8 wt% Cr, the harmful effect of W known in the literature was eliminated. It was found that the harmful effect of W can be suppressed by alloying sufficient amount of Cr and Al in this study. An increase in Ru was detrimental to the oxidation resistance by promoting the formation of NiO and NiWO4. It was also found that Ta and Re can show both positive and negative effects on the oxidation resistance depending on the contents of Cr as well as Al. Acknowledgements This research was supported by a grant from the Fundamental R&D Program for Strategic Core Materials (grant no. 10043795) funded by the Ministry of Trade, Industry and Energy, Republic of Korea. References [1] A. Klenk, K. Maile, H. Theofel, A. Helmrich, R. Husemann, J. Heinemann,
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ge Conference on Materials for G. Lüdenbach, in: Proceedings of the 8th Lie Advanced Power Engineering, vol. 2006, 2006, pp. 1597e1608. Astrid Heckl, Ralf Rettig, Robert F. Singer, Adv. Mater. Res. 278 (2011) 339e344. F.H. Latief, K. Kakehi, H. An-Chou Yeh, H. Murakami, Mater. Sci. Eng. A 592 (2014) 143e152. M.V. Nathal, L.J. Ebert, Metall. Trans. A 16 (1985) 1863e1870. Jian Zhang, Jinguo Li, Tao Jin, Xiaofeng Sun, Zhuangqi Hu, Mater. Sci. Eng. A 527 (2010) 3051e3056. G. Brewster, I.M. Edmonds, S. Gray, Oxid. Met. 81 (2014) 345e356. Q. Feng, B. Tryon, L.J. Carroll, T.M. Pollock, Mater. Sci. Eng. A 458 (2007) 184e194. K. Kawagishi, A. Sato, H. Harada, A.-C. Yeh, Y. Koizumi, T. Kobayashi, Mater. Sci. Tech. 25 (2009) 271e275. Neil Birks, Gerald H. Meier, Fred S. Pettit, Introduction to the Hightemperature Oxidation of Metals, second ed., Cambridge University Press, 2006, pp. 86e88. Qingqing Yang, Weihao Xiong, Shiqi Li, Hongxia Dai, Jun Li, J. Alloys Compd. 506 (2010) 461e467. Wayne E. Bell, M. Tagami, J. Phys. Chem. 67 (1963) 2432e2436. J.E. Battles, G.E. Gundersen, R.K. Edwards, J. Phys. Chem. 72 (1968) 3963e3969. H.K.D.H. Bhadeshia, D.J.C. MacKay, L.E. Svensson, Mater. Sci. Tech. 11 (1995) 1046e1051. H. Fujii, D.J.C. MacKay, H.K.D.H. Bhadeshia, ISIJ Int. 36 (1996) 1373e1382. Y.S. Yoo, C.Y. Jo, C.N. Jones, Mater. Sci. Eng. A 336 (2002) 22e29. Dae Won Yun, S.M. Seo, H.W. Jeong, I.S. Kim, Y.S. Yoo, J. Alloys Compd. 587 (2014) 105e112. D. Poquillon, D. Monceau, Oxid. Met. 59 (2003) 409e431. G.E.P. Box, D.W. Behnken, Technometrics 2 (1960) 455e475. R. Orosz, U. Krupp, H.-J. Christ, D. Monceau, Oxid. Met. 68 (2007) 165e176. Sherwin W. Yang, Oxid. Met. 15 (1981) 375e397. A. Sato, Y.-L. Chiu, R.C. Reed, Acta Mater. 59 (2011) 225e240. Md. Moniruzzaman, M. Maeda, Y. Murata, M. Morinaga, ISIJ Int. 43 (2003) 386e393. Dae Won Yun, S.M. Seo, H.W. Jeong, Y.S. Yoo, Corros. Sci. 83 (2014) 176e188. C.M. Younes, G.C. Allen, J.A. Nicholson, Corros. Eng. Sci. Tech. 42 (2007) 80e88. A. Akhtar, M.S. Hook, R.C. Reed, Metall. Mater. Trans. A 36 (2005) 3001e3017. C.T. Liu, J. Ma, X.F. Sun, J. Alloys Compd. 491 (2010) 522e526. Hee-Soo Kim, Si-Jun Park, Seong-Moon Seo, Young-Soo Yoo, Hi-Won Jeong, HeeJin Jang, Met. Mater. Int. 22 (2016) 789e796. M.H. Li, X.F. Sun, T. Jin, H.R. Guan, Z.Q. Hu, Oxid. Met. 60 (2003) 195e210. G.N. Irving, J. Stringer, D.P. Whittle, Corros. Sci. 15 (1975) 337e344. C. Wagner, J. Electrochem. Soc. 103 (1956) 627e633. C. Wagner, Corros. Sci. 5 (1965) 751e764. Dae Won Yun, Hyung Suk Seo, Jae Ho Jun, Jae Myung Lee, Kyoo Young Kim, Int. J. Hydrogen Energy 37 (2012) 10328e10336. S.P. Garg, N. Krishnamurthy, A. Awasthi, M. Venkatraman, J. Phase Equilib. 17 (1996) 63e77. F.N. Mazandarany, R.D. Pehlke, J. Electrochem. Soc. 121 (1974) 711e714. R.M. Neal, Retrieved June 24, 2013, http://www.cs.utoronto.ca/~radford/. D.E. Rumelhart, G.E. Hinton, R.J. Williams, Nature 323 (1986) 533e536. R.P. Lippmann, IEEE Acoust. Speech Signal Process. Mag. 4 (1987) 4e22.
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Journal of Alloys and Compounds journal homepage: http://www.elsevier.com/locate/jalcom
Effect of refractory elements and Al on the high temperature oxidation of Ni-base superalloys and modelling of their oxidation resistance Dae Won Yun, Seong Moon Seo, Hi Won Jeong, Young Soo Yoo* Korea Institute of Materials Science, 797 Changwondaero, Changwon, Gyungnam 641-831, South Korea
a r t i c l e i n f o
a b s t r a c t
Article history: Received 23 November 2016 Received in revised form 20 February 2017 Accepted 17 March 2017 Available online 19 March 2017
The oxidation resistance of Ni-Co-Cr-Al-W-Mo-Ta-Re-Ru alloys is evaluated by cyclic oxidation at 1100 C and modelled using an artificial neural network. The database required for the modelling was constructed using design of experiment (the Box-Behnken method) followed by oxidation experiments. The obtained model with a 7-10-1 architecture exhibits consistent prediction of the experimental data (R ¼ 0.999). Cr and Al enhance the oxidation resistance by promoting the formation of a protective NiAl2O4 layer. Mo and Ru are detrimental to the oxidation resistance. W, Ta and Re exhibit complex behaviours depending on the contents of other alloying elements. © 2017 Elsevier B.V. All rights reserved.
Keywords: High-temperature alloys Superalloys Nickel Oxidation Modelling SEM
1. Introduction Gas turbines are key systems in both the aerospace and thermal power plant industries. Since gas turbine components are exposed to high mechanical loading and severe environmental attack at high temperatures, Ni-base superalloys are widely used in gas turbine engines. Increasing the turbine inlet temperature results in a favourable reduction in the fuel cost and emission by enhancing the efficiency of gas turbine engines [1]. However, mechanical properties, such as the high temperature tensile strength and creep strength, and the environmental resistance, such as the oxidation resistance of materials for turbine components, should be further improved to increase the operating temperature. To enhance the high temperature tensile strength and creep strength of Ni-base superalloys, the content of refractory metals such as Mo, W, Ta, Re and Ru has been increased, and the content of Cr, which provides oxidation resistance, has been decreased. Although these compositional changes improve the mechanical properties [2e5], they can be detrimental to the oxidation resistance [6e8]. In particular, Ru, Mo and Re can form volatile oxide species; therefore, the oxidation resistance of alloys containing a large amount of these elements can
* Corresponding author. E-mail address: [email protected] (Y.S. Yoo). http://dx.doi.org/10.1016/j.jallcom.2017.03.179 0925-8388/© 2017 Elsevier B.V. All rights reserved.
be deteriorated [9e12]. Since both the mechanical and oxidation properties are important in superalloys, it is important to balance both properties in the development of superalloys. For a large number of alloying elements used in superalloys, computational modelling can be an effective tool in the development of superalloys with balanced mechanical and oxidation properties [13e16]. In a previous study on Ni-Cr-W-Mo alloys, artificial neural networks (ANN) were successfully employed to model the oxidation properties [16]. However, the growing number of variables exponentially increases the number of experiment required to generate the database for the modelling. For a full factorial design with 7 variables and 3 levels, 2187 samples have to be tested, which is practically impossible. Therefore, design of experiments (DOE) can solve this problem by reducing the number of experiments required to obtain relations between the variables and properties. By combining computational modelling with DOE, a promising tool can be created for the development of superalloys. In this study, 6 refractory elements (Cr, Mo, W, Ta, Re and Ru) and Al were selected as variables with a compositional range of Ni6Co-(2e8)Cr-(0e3)Mo-(2e10)W-(4.5e6.5)Al-(2e10)Ta-(0e6)Re(0e6)Ru in wt%. If a full factorial design were employed with 3 levels in each component, 2187 samples would be tested, as mentioned earlier. By using the Box-Behnken method, the number of samples required to generate the database for ANN modelling is reduced to 62 samples with 57 different compositions. In the Box-
D.W. Yun et al. / Journal of Alloys and Compounds 710 (2017) 8e19
9
Behnken design for 7 factors, 6 samples with identical chemical compositions with the centre point of the compositional range (Ni6Co-5Cr-1.5Mo-6W-5.5Al-6Ta-3Re-3Ru) should be tested due to the statistical importance. The oxidation resistance of the samples was evaluated by a cyclic oxidation test for 50 cycles with a one-hour dwell at 1100 C. The database of the weight changes after 50 cycles was constructed and modelled by using ANNs, which is similar to a previous study [16]. The model was successfully constructed in the Cr-Mo-W-AlTa-Re-Ru space and was consistent with the experimental data (with multiple correlation coefficient R ¼ 0.999). The weight change behaviour was analysed using a simple statistical spalling model proposed by Poquillon and Monceau [17]. The oxide microstructure was analysed with scanning electron microscopy (SEM) to study the effect of each element on the high temperature oxidation.
samples are prepared by electrical discharge cutting and microcutting to make discs with 10 mm diameters and 3 mm thicknesses. The samples were ground using SiC papers up to 1000-grit and cleaned in acetone and ethanol before the cyclic oxidation experiments. Cyclic oxidation is performed in an open-end furnace equipped with a programmable specimen in/out system. One cycle of experiments in this study is composed of heating at 1100 C for 60 min and cooling in open air for 30 min to room temperature. Fig. 1 shows the actual temperature profile during the cyclic oxidation experiment. The weights of samples are measured after 0, 2, 4, 16, 20, 32, 36 and 50 cycles using a digital balance with an accuracy of 104 g. Spalled oxide scales were not incorporated in the weight measurement. After the oxidation experiment, the oxide scale of the specimens was examined by SEM in the backscattered electron imaging (BEI) mode and energy dispersive spectroscopy.
2. Materials and methods
3. Results and discussion
In the alloy design framework, the goal was to find out the effect of alloying elements including Cr, Mo, W, Al, Ta, Re, and Ru on the oxidation resistance of Ni-base superalloy. The Box-Behnken design was employed to DOE to minimize the number of samples in this study. This design scheme requires smaller experiment runs than the other schemes such as central composite design (CCD); the CCD gives 152 runs for 7 independent variables while the Box-Behnken design [18] gives 62 runs for the same number of variables. In Box-Behnken design, the levels of the concentrations of alloying elements were determined according to three coded levels designated as 1, 0, and þ1. These codes represent the lower limit, the central value, and the upper limit of an independent variable, respectively. The zero level is presented as the central design point, corresponding to the central value of the range of independent variables under interest. The central points were repeated for 6 times to stabilize the extrapolation around the central region. The 1 and þ1 levels are edge points which correspond to the maximum and the minimum values to be tested. The compositional ranges of tested alloys are shown along with the coded levels in Table 1. The lower limit and the upper limit of each factor represent reasonable range of alloying elements in single crystal Ni-base superalloys. 62 alloy compositions were designed according to the Box-Behnken method for 7 alloying elements (Table 2). The alloys from A57 to A62 have an identical composition as a centre point of the compositional range. To save space, the weight changes after 50 cycles, which are used in ANN modelling, are also listed in Table 2. The samples were prepared by vacuum arc melting. Impurities that are detrimental to the oxidation resistance, such as S, are strictly controlled during the raw material selection and the melting process under 1 ppm. From each ingot, the oxidation
Table 1 Level values used in the Box-Behnken method. Level
Cr Al W Mo Ta Re Ru Co Ni
1
0
þ1
2 4.5 2 0 2 0 0 e e
5 5.5 6 1.5 6 3 3 6 Bal.
8 6.5 10 3 10 6 6 e e
3.1. Weight changes during cyclic oxidation The weight change of selected experimental alloys during the cyclic oxidation is shown in Fig. 2. One thermal cycle consisted of exposure at 1100 C for 1 h and cooling for 30 min to room temperature. The alloys shown in Fig. 2 contain 5 wt% Cr except A47 and A48. The alloys with 8 wt% Cr or 5 wt% Cr þ 6.5 wt% Al, including the alloys that are not presented in Fig. 2, showed no significant weight gain or loss during the experiment, which indicates excellent oxidation resistance. The oxidation resistance of these alloys were better than that of CMSX-4, which is known to have excellent oxidation resistance among single crystal Ni-base superalloys, in the literature [19]. The other alloys showed significant amounts of weight loss. In general, the addition of Cr and Al was beneficial to the oxidation resistance. The addition of Ta was slightly beneficial in some cases. Mo, W and Ru were detrimental to the oxidation resistance as known in the literature [6e8]. Re had no significant effect on the oxidation resistance. For an in-depth study of the effect of alloying elements, the weight change after 50 cycles of the oxidation experiment with respect to the content of each alloying element is plotted in Fig. 3. An increase in the Cr content from 2 wt% to 8 wt % significantly improved the oxidation resistance (Fig. 3(a)). An increase in the Al content from 4.5 wt% to 6.5 wt% also improved the oxidation resistance, except for the alloys with 8 wt% Cr (Fig. 3(b)). Mo addition up to 3 wt% was detrimental to the oxidation resistance. However, its detrimental effect was reduced when the content of W was 2 wt% (Fig. 3(c)). The effect of W on the oxidation resistance was generally negative. However, its negative effect was diminished when the contents of Cr and Al were sufficiently high, as in the case of the alloys with 8 wt% Cr and 5 wt% Cr þ 6.5 wt% Al (Fig. 3(d)). The addition of Ta was somewhat beneficial up to 10 wt %, except for the alloys with 2 wt% Cr or 8 wt% Cr (Fig. 3(e)). For the alloys with 2 wt% Cr, the addition of Ta showed a negative effect. Moreover, no significant effect was observed for alloys with 8 wt% Cr. Both positive and negative effects of Ta on the oxidation resistance of Ni-base alloys were reported in the literature [20,21]. The effect of Re on the oxidation resistance was not monotonous, and it differed depending on the content of Cr and Al (Fig. 3(f)). Moniruzzaman et al. reported that the harmful effect of Re was altered by the content of Al [22], which is similar results with this study. The addition of up to 6 wt % Ru resulted in an adverse effect on the oxidation resistance. Similar to the effect of W, the adverse effect of Ru was reduced when the contents of Cr and Al were sufficiently high (Fig. 3(g)).
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Table 2 Nominal alloy composition used in this study and their oxidation properties. The compositions are expressed in weight percent (wt%). Alloy Name
Cr
Mo
W
Al
Ta
Re
Ru
Co
Ni
Weight change after 50 cycles (mg/cm2)
A01 A02 A03 A04 A05 A06 A07 A08 A09 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 A27 A28 A29 A30 A31 A32 A33 A34 A35 A36 A37 A38 A39 A40 A41 A42 A43 A44 A45 A46 A47 A48 A49 A50 A51 A52 A53 A54 A55 A56 A57 A58 A59 A60 A61 A62
5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 2.0 8.0 2.0 8.0 2.0 8.0 2.0 8.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 2.0 8.0 2.0 8.0 2.0 8.0 2.0 8.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 2.0 8.0 2.0 8.0 2.0 8.0 2.0 8.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0
1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 0.0 3.0 0.0 3.0 0.0 3.0 0.0 3.0 0.0 0.0 3.0 3.0 0.0 0.0 3.0 3.0 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 0.0 3.0 0.0 3.0 0.0 3.0 0.0 3.0 1.5 1.5 1.5 1.5 1.5 1.5
6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 2.0 10.0 2.0 10.0 2.0 10.0 2.0 10.0 2.0 2.0 10.0 10.0 2.0 2.0 10.0 10.0 2.0 2.0 10.0 10.0 2.0 2.0 10.0 10.0 6.0 6.0 6.0 6.0 6.0 6.0
4.5 6.5 4.5 6.5 4.5 6.5 4.5 6.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 4.5 4.5 4.5 4.5 6.5 6.5 6.5 6.5 4.5 4.5 6.5 6.5 4.5 4.5 6.5 6.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5
2.0 2.0 10.0 10.0 2.0 2.0 10.0 10.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 2.0 2.0 10.0 10.0 2.0 2.0 10.0 10.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 2.0 2.0 2.0 2.0 10.0 10.0 10.0 10.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0
0.0 0.0 0.0 0.0 6.0 6.0 6.0 6.0 0.0 0.0 6.0 6.0 0.0 0.0 6.0 6.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 0.0 0.0 0.0 0.0 6.0 6.0 6.0 6.0 3.0 3.0 3.0 3.0 3.0 3.0
3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 0.0 0.0 0.0 0.0 6.0 6.0 6.0 6.0 0.0 0.0 0.0 0.0 6.0 6.0 6.0 6.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 0.0 0.0 0.0 0.0 6.0 6.0 6.0 6.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0
6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0
bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal. bal.
39.6 6.8 18.7 3.5 28.0 14.4 27.3 0.0 42.4 3.3 28.7 2.3 66.6 3.2 59.2 3.8 8.1 18.8 4.0 10.6 17.2 32.0 13.7 20.1 85.5 2.0 71.8 3.1 18.5 7.9 34.1 2.0 21.5 30.7 12.3 7.9 36.3 49.6 12.1 8.2 22.0 8.2 56.3 3.5 64.3 2.3 42.2 0.0 10.7 16.0 9.2 21.7 10.9 14.1 16.6 21.5 18.0 21.7 20.5 17.5 22.2 20.5
Prior to ANN analysis, linear model analysis was also performed with the weight changes of the alloys with 5 wt% Cr and 5.5 wt% Al after the cyclic oxidation to investigate the effect of each refractory element on the oxidation resistance; the result is presented in Fig. 4. The output value was calculated by the following equation. The linear coefficients and the constant were determined to minimize the sum of squared deviations.
yðxÞ ¼
I X
wi xi þ c
(1)
i¼1
xi is the weight percent of each alloying element as the input value, y(x) is the weight change after 50 cycles as the output value, wi and c are the linear coefficient for each alloying element and the constant for the model.
D.W. Yun et al. / Journal of Alloys and Compounds 710 (2017) 8e19
11
spallation. Mo and Ru deteriorated the oxidation resistance by increasing spallation. Ta showed a beneficial effect on reducing spallation. Furthermore, the addition of up to 6 wt% Re was negligible to the oxidation resistance. The effect of W could not be analysed by the model due to the incompatibility of the fitted curves by the model with the experimental data. 3.2. Microstructure of the oxide scale
Fig. 1. The actual temperature profile during the cyclic oxidation experiment.
Fig. 2. Weight change with oxidation cycles of one-hour exposures at 1100 C for selected experimental alloys and CMSX-4 from the literature [19].
The other composition range of Cr and Al was not analysed, as their data points were not sufficient for the analysis. The calculated value from the linear model showed a reasonable agreement with the experimental data (Fig. 4(a)). Mo and Ru were detrimental to the oxidation resistance, and W also showed a negative effect. Re only had a small negative effect on the oxidation resistance, and Ta was beneficial to the oxidation resistance in a given composition range (Fig. 4(b)). To separately observe the effect of alloying elements on the spallation of an oxide scale and the oxidation rate, the weight change data were analysed using a simple statistical model [17]. Some assumptions in the model are not valid in this study. However, the spalling probability and the parabolic rate constant obtained by the model can be useful for a study that compares the cyclic oxidation of alloys with similar chemical compositions. Moreover, the model was successful in our previous study for analysing the effect of Si and Al on the oxidation resistance of Ni-CrW-Mo alloys [23]. The fitted lines in Fig. 2 show the plausibility of the model. The spalling probability and the parabolic rate constant of selected experimental alloys obtained by the model are presented in Fig. 5. Cr was effective in reducing both spallation and the oxidation rate, whereas Al was mainly effective in reducing
After the cyclic oxidation experiment, the oxide scale of the specimens was examined by SEM. To observe the effect of the alloying element on the microstructure of oxide scale, the oxide microstructure of selected specimens were compared. The micrographs show the representative microstructure of the oxide scale where the oxide scales was relatively intact. The areas where the oxide scale was spalled out were not shown, because they do not show the microstructure of the oxide scales properly. In the areas where the oxide scale was relatively intact, the microstructure of the oxide scale was quite uniform. Since the addition of up to 6 wt% Re had negligible effect on the oxide microstructure, micrographs that compare the oxide microstructure of specimens with and without Re are not presented. The oxide phases were mainly identified by energy dispersive spectroscopy (EDS) analysis. Analysis results of X-ray diffraction and EDS in previous studies with similar oxide microstructure [16,23] and analysis results in the literature [24e29] were taken into consideration when identifying the oxide phases. Fig. 6 shows the effect of Cr on the oxide microstructure. The typical oxide scale of specimen A41 with 2 wt% Cr consisted of a thick mixed oxide layer, a Ni-Cr spinel layer and a discrete NiAl2O4 layer. In addition, the mixed oxide layer was composed of NiO, NiCr spinels and NiWO4 particles, which appear as white particles (Fig. 6(a)). The outermost part of the oxide scale, which is presumably a NiO layer, is seemingly spalled during the cooling process. In contrast, the oxide scale of specimen A42 with 8 wt% Cr consisted of a thin mixed oxide layer, a Ni-Cr spinel layer and a continuous NiAl2O4 layer. The mixed oxide layer was mainly composed of Ni-Cr spinels, and it contains a small amount of NiO and NiWO4 particles (Fig. 6(b)). The increase in the Cr content suppressed the formation of the transient oxides of NiWO4 and NiO and promoted the formation of a protective NiAl2O4 layer by acting as an oxygen getter [30,31]. Fig. 7 shows the effect of Al on the oxide microstructure. The oxide scale of specimen A01 with 4.5 wt% Al mainly consisted of a NiO layer and a mixed oxide layer. The mixed layer was composed of either NiO-NiWO4 (Fig. 7(a)) or NiO-spinel-NiWO4 (Fig. 7(b)). The innermost part of the oxide scale consisted of either an internal oxidation zone of spinels (Fig. 7(a)) or a discrete NiAl2O4 layer (Fig. 7(b)). Cracks were observed along the mixed oxide layer, which aggravate spallation of the oxide scale. On the contrary, the oxide scale of specimen A02 with 6.5 wt% Al consisted of a mixed oxide layer, a Ni-Cr spinel layer and a continuous NiAl2O4 layer. Moreover, the mixed oxide layer was composed of NiO, Ni-Cr spinels and NiWO4 particles (Fig. 7(c)). A small amount of NiO was also found on the outermost part of the oxide scale. The increase in the Al content promoted the formation of a protective NiAl2O4 layer, and it also reduced the formation of the mixed oxide layer, which is prone to spallation. Fig. 8 shows the effect of Mo on the oxide microstructure. In both the specimens of A49 without any Mo and A50 with 3 wt% Mo, the oxide scales were composed of a NiO layer, a mixed oxide layer, a Ni-Cr spinel layer and a NiAl2O4 layer. Furthermore, the formation of oxide nodules was observed in some areas of both specimens (Fig. 8(a), (c) and (d)). However, the size of the oxide nodules formed on the specimen with 3 wt% Mo was much larger, and the
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Fig. 3. Weight change after 50 cycles of the oxidation experiment with respect to the content of (a) Cr, (b) Al, (c) Mo, (d) W, (e) Ta, (f) Re and (g) Ru.
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Fig. 4. Linear model analysis result for the weight changes after the cyclic oxidation of the alloys with 5 wt% Cr and 5.5 wt% Al: (a) experimental and calculated weight changes and (b) linear coefficient of each alloying element.
Fig. 5. Parabolic rate constant and spalling probability of selected experimental alloys calculated by the simple statistical spalling model.
nodules were more frequently observed. It seems that the evaporation of Mo species deteriorated the stability of the protective oxide scale. This promoted the formation of oxide nodules. A similar effect of Mo was found in a study with ferritic stainless steels [32]. Fig. 9 shows the effect of W on the oxide microstructure. In both specimens of A53 with 2 wt% W and A55 with 10 wt% W, the oxide scales consisted of a NiO layer, a mixed oxide layer, a Ni-Cr spinel layer and a continuous NiAl2O4 layer. However, thick layers of NiO and NiO-spinel-NiWO4 mixed oxides were found in several areas of specimen A55 with 10 wt% W (Fig. 9(c)). Since this mixed oxide layer contains a large amount of pores, it is prone to cracking and spalling (Fig. 9(d)). An increase in the W content from 2 wt% to 10 wt% facilitated the growth of NiO and the mixed oxide layer. Fig. 10 shows the effect of Ta on the oxide microstructure. The oxide scale of specimen A17 with 2 wt% Ta mainly consisted of a NiO layer, a mixed oxide layer, Ni-Cr spinels and a NiAl2O4 layer (Fig. 10(a)). The formation of a discrete Al2O3 layer was observed in only a limited area (Fig. 10(b)). In specimen A19 with 10 wt% Ta, the formation of NiO and NiWO4 was suppressed; moreover, CrTaO4 was found beneath or inside the NiAl2O4 layer (Fig. 10(c) and (d)).
Fig. 6. BEI cross-sectional microstructure of (a) specimen A41 with 2 wt% Cr and (b) specimen A42 with 8 wt% Cr after 50 cycles of cyclic oxidation.
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Fig. 7. BEI cross-sectional microstructure of (a) and (b) specimen A01 with 4.5 wt% Al and (c) specimen A02 with 6.5 wt% Al after 50 cycles of cyclic oxidation.
Fig. 8. BEI cross-sectional microstructure of (a) and (b) specimen A49 without Mo and (c) and (d) specimen A50 with 3 wt% Mo after 50 cycles of cyclic oxidation.
D.W. Yun et al. / Journal of Alloys and Compounds 710 (2017) 8e19
Fig. 9. BEI cross-sectional microstructure of (a) specimen A53 with 2 wt% W and (b), (c) and (d) specimen A55 with 10 wt% W after 50 cycles of cyclic oxidation.
Fig. 10. BEI cross-sectional microstructure of (a) and (b) specimen A17 with 2 wt% Ta and (c) and (d) specimen A19 with 10 wt% Ta after 50 cycles of cyclic oxidation.
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Fig. 11. BEI cross-sectional microstructure of (a) and (b) specimen A19 without Ru and (c) and (d) specimen A23 with 6 wt% Ru after 50 cycles of cyclic oxidation.
Fig. 12. Real and predicted values of the cyclic oxidation weight change. The prediction was made by the ANN model with a 7-10-1 architecture. Error bars are also provided.
The formation of CrTaO4 in Ni- and Co-base alloys with Ta and Cr was reported in the literature [26e29]. Another change in the oxide microstructure was the formation of a continuous Al2O3 layer.
Compared to specimen A17 with 2 wt% Ta, the formation of Al2O3 was more frequently observed in specimen A19 with 10 wt% Ta. Additionally, areas with a continuous Al2O3 layer, as shown in Fig. 10(d), were also found in specimen A19. Ta presumably acted as an oxygen getter similar to Cr, which promoted the formation of a continuous Al2O3 layer. Since the equilibrium oxygen partial pressure of Ta (8.3 1023 atm [33]) is slightly lower than that of Cr (2.6 1020 atm [34]) at 1100 C, Ta can also act as an oxygen getter for the formation of Al2O3. Fig. 11 shows the effect of Ru on the oxide microstructure. In specimen A19 without any Ru, the oxide scale mainly consisted of a Cr-Ni spinel layer and a NiAl2O4 layer, and NiO and NiWO4 were found in some areas (Fig. 11(a)). Moreover, the formation of NiO and NiWO4 was suppressed in the area with a CrTaO4 layer (Fig. 11(b)). However, a thick NiO layer and a mixed oxide layer, composed of NiO, Ni-Cr spinels and NiWO4, were formed on specimen A23 with 6 wt% Ru (Fig. 11(c)). The formation of NiWO4 particles was observed in even the area with a CrTaO4 layer (Fig. 11(d)). Similar to Mo, Ru seems to deteriorate the stability of the protective oxide scale and to promote the formation of NiO and NiWO4 by forming volatile oxide species. The changes in the oxide microstructure with respect to the changes in the content of each alloying element were consistent with the weight change data of Fig. 2. Cr and Al improved the oxidation resistance by promoting the formation of protective NiAl2O4 and by suppressing the formation of transient oxides such as NiO and NiWO4. Mo and Ru disturbed a barrier property of the oxide scale and promoted the formation of transient oxides by forming volatile oxide species such as MoO3 and RuO3 [9e12]. W also deteriorated the oxidation resistance by accelerating the
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Fig. 13. Contour maps of the weight change after 50 cycles in terms of the W and Mo contents for Ni-6Co-xCr-yAl-6Ta-3Re-3Ru alloys generated by the ANN prediction. A scale bar indicating the weight change value is located on the right side of each contour map.
formation of the mixed oxide layer containing NiWO4. Ta enhanced the oxidation resistance by encouraging the formation of a continuous Al2O3 layer and by suppressing the formation of transient oxides. The addition of up to 6 wt% Re had a negligible effect on the oxidation resistance and the microstructure. 3.3. Artificial neural network modelling The cyclic oxidation database is constructed by setting contents of Cr, Al, Mo, W, Ta, Re and Ru as the input parameters and the weight change after 50 cycles as the output (Table 2). A program by Radford Neal [35] was used in the ANN training and prediction in this study. The output value, y(x), is calculated by following equations.
yðxÞ ¼
H X
gh vh þ bo
(2)
h¼1
gh ðxÞ ¼ tanh
I X i¼1
! xi wih þ bh
(3)
Here, xi and gh(x) are the input value and the value of the hidden node, wih and bh are the weight and the bias on the connection from input i to hidden node h, vh and bo are the weight and the bias on the connection from hidden node h to output. The values of the weights and the biases are randomly assigned as an initial value. By neural network training process, the values of the weights and the biases are adjusted to predict the output value properly. Detailed information on ANN training can be found in previous studies [15,16]. Only one hidden layer is employed in this study, which can be expressed as ‘7-x-1’ with ‘x’ as the number of nodes in the hidden layer. A total of 46 architectures from ‘7-5-1’ to ‘7-50-1’ were tested to find a model consistent with the actual experimental data while preventing over-fitting [36,37]. Among the tested architectures, a 7-10-1 architecture showed the highest value of the multiple correlation coefficient R (R ¼ 0.999) and was chosen for the study. The real and predicted weight change data are shown in Fig. 12. The predicted value by ANN modelling shows good agreement with the experimental data. The short error bars in Fig. 12 also indicate that each prediction is performed with very high confidence. Figs. 13 and 14 show contour maps of the weight change after 50 cycles predicted by the ANN model, which are a part of the ANN
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Fig. 14. Contour maps of the weight change after 50 cycles in terms of the Re and Ru contents for Ni-6Co-xCr-yAl-6Ta-6W-1.5Mo alloys generated by the ANN prediction. A scale bar indicating the weight change value is located on the right side of each contour map.
modelling results. The contour maps in Fig. 13 were plotted by changing the W content from 2 to 10 wt% and the Mo content from 0 to 3 wt% with a step size of 0.1 wt% for various Cr and Al contents. As observed in the scale bar of the contour maps, the overall weight loss was reduced as the Cr and Al contents increased. However, the effect of Mo and W varied with the Cr and Al contents. In alloys with 2 wt% Cr and 4.5 wt% Al, it was predicted that an increase in the W and Mo contents would result in a reduction in the weight loss. A similar behaviour was observed in a study with Ni-Cr-W-Mo alloys [16]. The reduced thermal expansion mismatch between a metal substrate and an oxide scale was proposed as a mechanism for the reduction in the weight loss of alloys with low Cr content and high W and Mo contents [16]. In alloys with more than 5 wt% Cr or 5.5 wt % Al, an increase in the W and Mo contents was generally harmful for the oxidation resistance. As observed from Fig. 3(c), the harmful effect of Mo was reduced with 2 wt% W in alloys with 5 wt% Cr and 5.5 wt% Al. In alloys with 5 wt% Cr þ 6.5 wt% Al and alloys with 8 wt % Cr, however, the harmful effect of W on the oxidation resistance was diminished. The contour maps in Fig. 14 were plotted by changing the Re content from 0 to 6 wt% and the Ru content from 0 to 6 wt% with a step size of 0.1 wt% for various Cr and Al contents. In the same
manner as Fig. 13, the overall weight loss was reduced as the Cr and Al contents increased. Ru was detrimental to the oxidation resistance in all cases. However, the effect of Re was varied depending on the Cr and Al contents. In alloys with 2 wt% Cr and 5 wt% Cr þ 4.5 wt% Al, it was predicted that the addition of up to 6 wt% Re would be slightly beneficial to the oxidation resistance. In alloys with 8 wt% Cr and 6.5 wt% Al, the addition of Re also had a beneficial effect. In contrast, the addition of Re was harmful to the oxidation resistance in alloys with 5 wt% Cr þ 6.5 wt% Al and 8 wt% Cr þ (4.5e5.5) wt% Al. In alloys with 5 wt% Cr and 5.5 wt% Al, Re only had a small amount of negative effect on the oxidation resistance. This is consistent with the result of the linear model analysis (Fig. 4). As observed from Figs. 13 and 14, the effect of an alloying element can be altered by the contents of other alloying elements. Since there is complex interaction between the alloying elements, in most cases, their behaviour cannot be fitted by a simple linear model. Moreover, modelling becomes more difficult as the number of variables increases. By using ANN, however, the oxidation resistance was successfully modelled in the complex system with 7 variables. This combined method of DOE and ANN can be a useful tool in the development of not only superalloys but also other
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complex alloys. 4. Conclusions
[2] [3]
To model the oxidation resistance of Ni-base superalloys with a chemical composition range of Ni-6Co-(2e8)Cr-(0e3)Mo-(2e10) W-(4.5e6.5)Al-(2e10)Ta-(0e6)Re-(0e6)Ru with ANN, weight changes were measured after 50 cycles of a one-hour dwell at 1100 C. The Box-Behnken method was used to reduce the number of samples required to generate the database for the modelling. Compared to the full factorial design, the required number of samples reduced from 2187 to 62 by this method. The obtained ANN model had a 7-10-1 architecture and a multiple correlation coefficient of 0.999. An increase in Cr and Al was beneficial to the oxidation resistance by promoting the formation of a protective NiAl2O4 layer and by suppressing the formation of transient oxides such as NiO and NiWO4. An increase in Mo and W was generally harmful to the oxidation resistance by promoting the formation of NiO and a porous mixed oxide layer that consisted of NiWO4 and Ni-Cr spinel, except in the alloys with 2 wt% Cr and 4.5 wt% Al. In those alloys, an increase in Mo and W reduced the weight loss. In alloys with 5 wt% Cr þ 6.5 wt% Al and alloys with 8 wt% Cr, the harmful effect of W known in the literature was eliminated. It was found that the harmful effect of W can be suppressed by alloying sufficient amount of Cr and Al in this study. An increase in Ru was detrimental to the oxidation resistance by promoting the formation of NiO and NiWO4. It was also found that Ta and Re can show both positive and negative effects on the oxidation resistance depending on the contents of Cr as well as Al. Acknowledgements This research was supported by a grant from the Fundamental R&D Program for Strategic Core Materials (grant no. 10043795) funded by the Ministry of Trade, Industry and Energy, Republic of Korea. References [1] A. Klenk, K. Maile, H. Theofel, A. Helmrich, R. Husemann, J. Heinemann,
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