J. Mater. Sci. Technol. ›› 2020, Vol. 49: 202-210.DOI: 10.1016/j.jmst.2020.01.044
• Research Article • Previous Articles Next Articles
Yuanjie Zhia, Tao Yanga,*(), Dongmei Fua,b,*()
Received:
2019-06-30
Revised:
2020-01-13
Accepted:
2020-01-25
Published:
2020-07-15
Online:
2020-07-17
Contact:
Tao Yang,Dongmei Fu
Yuanjie Zhi, Tao Yang, Dongmei Fu. An improved deep forest model for forecast the outdoor atmospheric corrosion rate of low-alloy steels[J]. J. Mater. Sci. Technol., 2020, 49: 202-210.
Alloy | Element Compositions (wt.%) | |||||||
---|---|---|---|---|---|---|---|---|
Mn | S | P | Si | Cr | Cu | Ni | Fe | |
06CuPCrNiMo | 0.40 | 0.023 | 0.050 | 0.17 | 0 | 0.17 | 0 | Balance |
09CuPCrNiA | 0.40 | 0.023 | 0.015 | 0.26 | 0.10 | 0.05 | 0.02 | Balance |
09CuPTiRe | 0.40 | 0.019 | 0.080 | 0.28 | 0 | 0.29 | 0 | Balance |
09MnNb(s) | 1.18 | 0.024 | 0.027 | 0.20 | 0.10 | 0.05 | 0.10 | Balance |
10CrCuSiV | 0.31 | 0.002 | 0.010 | 0.62 | 0.83 | 0.25 | 0.10 | Balance |
10CrMoAl | 0.45 | 0.002 | 0.012 | 0.35 | 0.98 | 0.09 | 0 | Balance |
14MnMoNbB | 1.53 | 0.010 | 0.022 | 0.34 | 0.10 | 0.05 | 0 | Balance |
15MnMoVN | 1.52 | 0.004 | 0.026 | 0.40 | 0.10 | 0.05 | 0 | Balance |
16Mn | 1.40 | 0.025 | 0.009 | 0.36 | 0.10 | 0.05 | 0 | Balance |
16MnQ | 1.37 | 0.023 | 0.030 | 0.30 | 0.10 | 0.07 | 0.05 | Balance |
D36 | 1.40 | 0.018 | 0.022 | 0.39 | 0.05 | 0.05 | 0 | Balance |
JN235(RE) | 0.52 | 0.025 | 0.030 | 0.30 | 0.10 | 0.07 | 0 | Balance |
JN255 | 0.67 | 0.006 | 0.016 | 0.07 | 0.02 | 0.05 | 0.05 | Balance |
JN255(RE) | 0.39 | 0.005 | 0.010 | 0.62 | 0.83 | 0.25 | 0.10 | Balance |
JN345 | 0.39 | 0.005 | 0.110 | 0.05 | 0.90 | 0.40 | 0.65 | Balance |
JN345(RE) | 0.36 | 0.011 | 0.089 | 0.28 | 0 | 0.29 | 0 | Balance |
JY235(RE) | 0.27 | 0.010 | 0.089 | 0.28 | 0 | 0.29 | 0 | Balance |
Table 1 Element compositions of 17 LAS in collected datasets.
Alloy | Element Compositions (wt.%) | |||||||
---|---|---|---|---|---|---|---|---|
Mn | S | P | Si | Cr | Cu | Ni | Fe | |
06CuPCrNiMo | 0.40 | 0.023 | 0.050 | 0.17 | 0 | 0.17 | 0 | Balance |
09CuPCrNiA | 0.40 | 0.023 | 0.015 | 0.26 | 0.10 | 0.05 | 0.02 | Balance |
09CuPTiRe | 0.40 | 0.019 | 0.080 | 0.28 | 0 | 0.29 | 0 | Balance |
09MnNb(s) | 1.18 | 0.024 | 0.027 | 0.20 | 0.10 | 0.05 | 0.10 | Balance |
10CrCuSiV | 0.31 | 0.002 | 0.010 | 0.62 | 0.83 | 0.25 | 0.10 | Balance |
10CrMoAl | 0.45 | 0.002 | 0.012 | 0.35 | 0.98 | 0.09 | 0 | Balance |
14MnMoNbB | 1.53 | 0.010 | 0.022 | 0.34 | 0.10 | 0.05 | 0 | Balance |
15MnMoVN | 1.52 | 0.004 | 0.026 | 0.40 | 0.10 | 0.05 | 0 | Balance |
16Mn | 1.40 | 0.025 | 0.009 | 0.36 | 0.10 | 0.05 | 0 | Balance |
16MnQ | 1.37 | 0.023 | 0.030 | 0.30 | 0.10 | 0.07 | 0.05 | Balance |
D36 | 1.40 | 0.018 | 0.022 | 0.39 | 0.05 | 0.05 | 0 | Balance |
JN235(RE) | 0.52 | 0.025 | 0.030 | 0.30 | 0.10 | 0.07 | 0 | Balance |
JN255 | 0.67 | 0.006 | 0.016 | 0.07 | 0.02 | 0.05 | 0.05 | Balance |
JN255(RE) | 0.39 | 0.005 | 0.010 | 0.62 | 0.83 | 0.25 | 0.10 | Balance |
JN345 | 0.39 | 0.005 | 0.110 | 0.05 | 0.90 | 0.40 | 0.65 | Balance |
JN345(RE) | 0.36 | 0.011 | 0.089 | 0.28 | 0 | 0.29 | 0 | Balance |
JY235(RE) | 0.27 | 0.010 | 0.089 | 0.28 | 0 | 0.29 | 0 | Balance |
Factors | Average | Minimum | Maximum |
---|---|---|---|
Average Relative Humidity, RH (s%) | 75.09 | 56.17 | 87.71 |
Average Temperature,T (℃) | 17.58 | 11.08 | 26.05 |
Rainfall (mm/month) | 159.74 | 45.64 | 753.00 |
SO2 concentration, SO2 (mg/cm3) | 0.09 | 0.02 | 0.30 |
pH of rain (pH) | 6.14 | 5.11 | 6.97 |
Chloride concentration, Cl- (mg/cm3) | 0.22 | 0 | 1.97 |
Table 2 Environmental factors and their statistical values.
Factors | Average | Minimum | Maximum |
---|---|---|---|
Average Relative Humidity, RH (s%) | 75.09 | 56.17 | 87.71 |
Average Temperature,T (℃) | 17.58 | 11.08 | 26.05 |
Rainfall (mm/month) | 159.74 | 45.64 | 753.00 |
SO2 concentration, SO2 (mg/cm3) | 0.09 | 0.02 | 0.30 |
pH of rain (pH) | 6.14 | 5.11 | 6.97 |
Chloride concentration, Cl- (mg/cm3) | 0.22 | 0 | 1.97 |
Methods | Fitting Results (Training samples) | Generalization Results (Testing samples) | ||||
---|---|---|---|---|---|---|
MAPE (%) | RMSE | R2 | MAPE (%) | RMSE | R2 | |
ANN | 0.28 | 0.08 | 1.000 | 28.16 | 8.35 | 0.785 |
SVR | 3.66 | 3.33 | 0.960 | 25.16 | 7.45 | 0.823 |
RF | 6.02 | 2.32 | 0.980 | 16.21 | 5.46 | 0.908 |
RF-WKNNs | 6.02 | 2.32 | 0.980 | 15.31 | 5.36 | 0.911 |
cForest | 0.78 | 0.43 | 1.000 | 15.22 | 5.09 | 0.920 |
DCGF-WKNNs | 0.89 | 0.44 | 1.000 | 12.95 | 4.95 | 0.924 |
Table 3 Comparison of different methods for fitting and generalization.
Methods | Fitting Results (Training samples) | Generalization Results (Testing samples) | ||||
---|---|---|---|---|---|---|
MAPE (%) | RMSE | R2 | MAPE (%) | RMSE | R2 | |
ANN | 0.28 | 0.08 | 1.000 | 28.16 | 8.35 | 0.785 |
SVR | 3.66 | 3.33 | 0.960 | 25.16 | 7.45 | 0.823 |
RF | 6.02 | 2.32 | 0.980 | 16.21 | 5.46 | 0.908 |
RF-WKNNs | 6.02 | 2.32 | 0.980 | 15.31 | 5.36 | 0.911 |
cForest | 0.78 | 0.43 | 1.000 | 15.22 | 5.09 | 0.920 |
DCGF-WKNNs | 0.89 | 0.44 | 1.000 | 12.95 | 4.95 | 0.924 |
Fig. 5. Performances of different time periods of 6 methods: (a) RMSE results of 6 methods in sub-datasets with different exposure time; (b) R2 results of 6 methods in sub-datasets with different exposure time; (c) MAPE results of 6 methods in sub-datasets with different exposure time.
Fig. 6. Prediction corrosion rate curve with changing single environmental variable: Effects of pH thresholds (a), temperature thresholds (b), RH thresholds (c), SO2 thresholds (d), rainfall thresholds (e) and Cl- thresholds (f).
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