Prediction of the Hot Compressive Deformation Behavior for Superalloy
5. Prediction Capability Comparison between the BP-ANN Model and Arrhenius Type Constitutive Equation
Appl. Sci. 2016, 6, 66 9 of 16
(a) (b)
(c) (d)
Figure 6. Relationships between: (a) ; (b) lnA; (c) n; and (d) Q and true strain by polynomial fit.
Thus, the improved Arrhenius type model with variable coefficients can be expressed as Equation (13).
1 2 1
1 exp[ ( ) / 8.314 ] exp( ( ) / 8.314 ) 2
ln{( ) [( ) 1] }
( ) ( )
h h
j T j T
g f f
(13)
where g
, f ε ,
h
, j
are polynomial functions of strain for , A , n , Q .Applying the aforementioned material constants to Equation (13), the true stress values are calculated for the experimental temperature, strain and strain rate ranges. Figure 7 shows comparisons between the experimental data and the predicted results calculated from the developed
Table 2.Relative errors of the predicted results by the back-propagational artificial neural network (BP-ANN) model and constitutive equation to experimental results under the condition of 1100˝C and 0.01 s´1and 1200˝C and 1 s´1.
Strain Rate (s´1)
Temperature
(˝C) Strain True Stress (MPa) Equation Relative Error (%)
Experimental BP-ANN BP-ANN Equation
0.01 1100
0.05 65.08 63.59 64.37 ´2.29 ´1.08
0.10 71.80 71.58 71.18 ´0.31 ´0.86
0.15 75.24 74.75 72.79 ´0.64 ´3.25
0.20 76.09 75.31 72.42 ´1.03 ´4.83
0.25 75.35 74.99 72.30 ´0.47 ´4.04
0.30 73.81 74.05 71.59 0.33 ´3.01
0.35 72.06 72.75 71.23 0.96 ´1.15
0.40 70.57 71.31 70.43 1.04 ´0.20
0.45 69.47 69.93 71.33 0.67 2.68
0.50 68.73 68.77 70.15 0.05 2.06
0.55 68.32 67.90 70.40 ´0.62 3.04
0.60 68.17 67.36 70.40 ´1.18 3.28
0.65 68.20 67.13 69.34 ´1.56 1.68
0.70 68.34 67.13 70.24 ´1.78 2.77
0.75 68.54 67.26 69.70 ´1.87 1.69
0.80 68.72 67.39 68.91 ´1.93 0.28
0.85 68.80 67.37 69.76 ´2.08 1.39
0.90 68.73 67.08 67.71 ´2.41 ´1.49
1 1200
0.05 91.67 87.76 90.72 ´4.26 ´1.04
0.10 102.36 105.69 111.12 3.24 8.55
0.15 106.88 108.91 118.77 1.90 11.13
0.20 109.66 110.69 121.14 0.94 10.47
0.25 111.91 112.48 121.60 0.51 8.66
0.30 113.69 114.08 120.83 0.35 6.28
0.35 114.99 115.35 120.39 0.31 4.69
0.40 115.82 116.21 119.02 0.34 2.76
0.45 116.23 116.63 120.40 0.34 3.59
0.50 116.27 116.61 118.44 0.30 1.87
0.55 115.99 116.25 118.69 0.22 2.33
0.60 115.45 115.62 118.07 0.14 2.26
0.65 114.71 114.75 115.69 0.04 0.86
0.70 113.82 113.58 116.16 ´0.20 2.06
0.75 112.83 112.03 114.25 ´0.71 1.26
0.80 111.81 110.07 111.94 ´1.55 0.12
0.85 110.80 107.84 111.96 ´2.67 1.05
0.90 109.86 105.50 107.05 ´3.97 ´2.56
It is found in Table 2 that the relative percentage error obtained from BP-ANN model varies from ´4.26% to 3.24%, whereas it is in the range from ´4.83% to 11.13%
for the improved Arrhenius-type constitutive model. As shown in Figure 8a,b, the relative percentage errors have been summarized in which the height of the histogram expresses the relative frequency of the relative percentage errors. Through nonlinear curve fitting, the distributions of relative percentage errors obtained from Arrhenius-type model and BP-ANN model present a typical Gaussian distribution, which was expressed as in Equation (15). In the function, the two parameters of µand w represent the mean value and standard deviation, respectively, which are
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two of the most important indexes in statistical work. The mean value and standard deviation calculated by Equations (16) and (17) [6], respectively, reflect the central tendency and discrete degree of a set of data, and smaller value of w and µ close to 0 hint that better errors distribution is achieved. As shown in Figure 8a,b, the mean value (µ) and the standard deviation (w) of the Arrhenius-type model is 3.0012 and 2.0533, respectively, while the mean value (µ) and the standard deviation (w) of the BP-ANN model is 0.0714 and 0.2564, respectively, which indicate that the distribution of relative percentage errors obtained by the BP-ANN model is more centralized.
It suggests that the BP-ANN model has a good generalization capability.
y “ y0`Ae´
pδi´ µq2
2w2 (15)
µ “ 1 N
N
ÿ
i“1
δi (16)
w “ g f f
e 1
N ´ 1
N
ÿ
i“1
pδi´ µq2 (17)
where δi is a value of the relative error; µ, w, and y are the mean value, standard deviation and probability density of δ respectively; y0and A are constants, and N is the number of relative errors, here N = 36.
Appl. Sci. 2016, 6, 66 12 of 16
(a) (b)
Figure 8. The relative errors distribution on the true stress points predicted by (a) the BP‐ANN model and (b) the Arrhenius type constitutive equation relative to the experimental ones.
In addition, two commonly used statistical indicators of the correlation coefficient (R) and average absolute relative error (AARE) are introduced to check the ability and the predictability of the BP‐ANN model and Arrhenius‐type model, which are expressed by Equations (18) and (19) [6].
R is a numerical value between −1 and 1 that expresses the strength of the linear relationship between two variables. A high R‐value close to 1 illustrates that the predicted values conform to the experimental ones well. A value of 0 indicates that there is no relationship. Value close to −1 signal a strong negative relationship between the two variables. The average absolute relative error (AARE) is also computed through a term‐by‐term comparison of the relative error and thus is an unbiased statistical parameter to measure the predictability of a model [6,15]. Meanwhile, a low AARE‐value close to 0 indicates that the sum of the errors between the predicted and experimental values tends to be 0.
2 2
1
)( )
) ( )
N
i i
i 1
N N
i i
i 1 i
(E E P P
R
(E E P P
(18)1
1 N i i 100%
i i
AARE P E
N E
(%) (19)
where Ei is the sample of experimental value and Pi is the sample of predicted value. E is the mean value of experimental sample values, P is the mean value of predicted sample values, and N is the number of data which were employed in the investigation.
The correlation relationships between the experimental and respectively predicted true stress by BP‐ANN model and modified Arrhenius‐type constitutive equation were illustrated in Figure 9. It is discovered that the points in Figure 9, which take experimental true stress as horizontal axis and predicted true stress as vertical axis, lie fairly close to the best linear fitted line, suggesting that the predicted stress‐strain values conform very well to the homologous experimental ones. Besides, the R‐values for the predicted true stress of BP‐ANN and modified Arrhenius are 0.9998 and 0.9899, respectively, from another quantitative perspective proving the strong linear relationships between the predicted and experimental true stress. Additionally, the AARE‐values relative to the experimental true stress was calculated by Equation (19) and exhibited in Figure 9. According to the calculation results, it is manifest that the AARE‐value for the BP‐ANN model is 1.20%, but, for the constitutive equation, it reaches a higher level, 3.06%. Lower AARE‐value means a smaller deviation on the whole; therefore, the BP‐ANN model has higher accuracy in predicting the true stress of superalloy nimonic 80A than the constitutive equation.
-4 -2 0 2 4
0.0 0.3 0.6 0.9 1.2
1.5 μ(%)= 0.0714
w(%)= 0.2564
Relative frequency
Relative error (%)
-8 -4 0 4 8 12
0.00 0.06 0.12 0.18 0.24
0.30 μ(%)= 3.0012
w(%)= 2.0533
Relative frequency
Relative error (%)
Figure 8. The relative errors distribution on the true stress points predicted by (a) the BP-ANN model and (b) the Arrhenius type constitutive equation relative to the experimental ones.
In addition, two commonly used statistical indicators of the correlation coefficient (R) and average absolute relative error (AARE) are introduced to check the ability and the predictability of the BP-ANN model and Arrhenius-type model, which
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are expressed by Equations (18) and (19) [6]. R is a numerical value between ´1 and 1 that expresses the strength of the linear relationship between two variables. A high R-value close to 1 illustrates that the predicted values conform to the experimental ones well. A value of 0 indicates that there is no relationship. Value close to
´1 signal a strong negative relationship between the two variables. The average absolute relative error (AARE) is also computed through a term-by-term comparison of the relative error and thus is an unbiased statistical parameter to measure the predictability of a model [6,15]. Meanwhile, a low AARE-value close to 0 indicates that the sum of the errors between the predicted and experimental values tends to be 0.
R “
řN
i“1pEi´EqpPi´Pq b
řN
i“1pEi´Eq2řN
i“1pPi´Pq2
(18)
AARE p%q “ 1 N
ÿN i“1
ˇ ˇ ˇ ˇ
Pi´Ei
Ei ˇ ˇ ˇ ˇ
ˆ100% (19)
where Ei is the sample of experimental value and Pi is the sample of predicted value. E is the mean value of experimental sample values, P is the mean value of predicted sample values, and N is the number of data which were employed in the investigation.
The correlation relationships between the experimental and respectively predicted true stress by BP-ANN model and modified Arrhenius-type constitutive equation were illustrated in Figure 9. It is discovered that the points in Figure 9, which take experimental true stress as horizontal axis and predicted true stress as vertical axis, lie fairly close to the best linear fitted line, suggesting that the predicted stress-strain values conform very well to the homologous experimental ones. Besides, the R-values for the predicted true stress of BP-ANN and modified Arrhenius are 0.9998 and 0.9899, respectively, from another quantitative perspective proving the strong linear relationships between the predicted and experimental true stress. Additionally, the AARE-values relative to the experimental true stress was calculated by Equation (19) and exhibited in Figure 9. According to the calculation results, it is manifest that the AARE-value for the BP-ANN model is 1.20%, but, for the constitutive equation, it reaches a higher level, 3.06%. Lower AARE-value means a smaller deviation on the whole; therefore, the BP-ANN model has higher accuracy in predicting the true stress of superalloy nimonic 80A than the constitutive equation.
By several comparison methods, the performance of the two models can be concluded that the BP-ANN model has higher prediction accuracy than the improved Arrhenius-type model. It is valuable to note that, in the training stage of the BP-ANN model, the experimental stress-strain data of two test curves under the conditions of 1100˝C and 0.01 s´1and 1200˝C and 1 s´1did not participate. However, when establishing the constitutive equation, they were involved. However, even on this
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premise, the BP-ANN model still shows smaller errors, giving the full proof that the present BP-ANN model has better prediction capability than the constitutive equation in the flow characteristics of superalloy nimonic 80A.
Appl. Sci. 2016, 6, 66 13 of 16
(a) (b)
Figure 9. The correlation relationships between the predicted and experimental true stress for the (a) BP‐ANN model and (b) Arrhenius‐type model.
By several comparison methods, the performance of the two models can be concluded that the BP‐ANN model has higher prediction accuracy than the improved Arrhenius‐type model. It is valuable to note that, in the training stage of the BP‐ANN model, the experimental stress‐strain data of two test curves under the conditions of 1100 °C and 0.01 s−1 and 1200 °C and 1 s−1 did not participate.
However, when establishing the constitutive equation, they were involved. However, even on this premise, the BP‐ANN model still shows smaller errors, giving the full proof that the present BP‐ANN model has better prediction capability than the constitutive equation in the flow characteristics of superalloy nimonic 80A.