Laser & Optoelectronics Progress, Volume. 59, Issue 12, 1234001(2022)
Prediction of Cr, Mn, and Ni in Medium and Low Alloy Steels by GA-BP Neural Network Combined with EDXRF Technology
Figures & Tables(8)
Fig. 5. Error distributions of analysis results of three elements by GA-BP and FP methods. (a) Cr element; (b) Mn element; (c) Ni element
Table 1. Element content range of experimental samples of various alloy steels
View tableTable 1. Element content range of experimental samples of various alloy steels
Sample name Cr Mn Ni 18CrNiW 0.340-1.830 0.120-0.710 2.680-4.580 30CrMnSiNiA 0.587-1.592 0.744-1.607 0.971-2.064 38CrMoAl 0.995-2.028 0.161-0.691 0.069-0.443 CrWMn 0.319-1.354 0.172-1.280 0.037-0.245 GSBH40067-93 0.581-1.950 0.216-1.350 0.229-2.290 YSBS15301-94 0.313-1.520 0.292-1.310 0.521-3.180
Table 2. Important parameters of GA-BP neural network
View tableTable 2. Important parameters of GA-BP neural network
Parameter Numeric value Parameter Numeric value Training sample 108×3 Output layer Activation function purelin Test sample 36×3 Population size 45 Input neuron 3 Iteration ordinal Number 20 Hidden layer neuron 5 Learning rate 0.1 Output layer neuron 3 Training steps 1000 Training function trainlm Crossover rate 0.2 Hidden layer Activation function tansig Mutation rate 0.0025
Table 3. Prediction results of CrWMn series samples
View tableTable 3. Prediction results of CrWMn series samples
Sample Analysis method Cr Mn Ni Predicted
value
Relative standard
deviation
Predicted
value
Relative standard
deviation
Predicted
value
Relative standard
deviation
CrWMn1 Standard 0.319 1.280 0.037 FP 0.350 9.718 1.270 0.781 0.020 45.946 GA-BP 0.330 3.448 1.285 0.391 0.057 54.054 CrWMn2 Standard 0.444 0.959 0.088 FP 0.440 0.901 0.920 4.067 0.030 65.909 GA-BP 0.447 0.676 1.017 6.048 0.151 71.591 CrWMn3 Standard 0.763 0.883 0.131 FP 0.740 3.014 0.960 8.720 0.070 46.565 GA-BP 0.749 1.835 0.899 1.812 0.101 22.901 CrWMn4 Standard 1.060 0.347 0.191 FP 1.090 2.830 0.370 6.628 0.140 26.702 GA-BP 1.073 1.226 0.379 9.222 0.151 20.942 CrWMn5 Standard 1.354 0.172 0.245 FP 1.450 7.090 0.200 16.279 0.200 18.367 GA-BP 1.391 2.733 0.144 16.279 0.261 6.531
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Haisheng Song, Zhao Chen, Dacheng Xu, Rongwang Xu. Prediction of Cr, Mn, and Ni in Medium and Low Alloy Steels by GA-BP Neural Network Combined with EDXRF Technology[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1234001
Paper Information
Category: X-Ray Optics
Received: Apr. 30, 2021
Accepted: Jun. 27, 2021
Published Online: Jun. 9, 2022
The Author Email: Zhao Chen (chenzhao970316@163.com)
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