Grain Size Prediction of Aluminum Alloy Dies Castings Based on GA-ELM
Yi MEI1(), Quanlong SUN1, Lihua YU1, Chuanrong WANG2, Huaqiang XIAO1
1 College of Mechanical Engineering, Guizhou University, Guiyang 550025, China 2 China Petroleum Xinjiang Dushanzi Petrochemical Corp., Kelamayi 833699, China
Cite this article:
Yi MEI, Quanlong SUN, Lihua YU, Chuanrong WANG, Huaqiang XIAO. Grain Size Prediction of Aluminum Alloy Dies Castings Based on GA-ELM. Acta Metall Sin, 2017, 53(9): 1125-1132.
Effective grain size prediction for aluminum alloy die castings is of great significance to the rational formulation of die casting process parameters and to the improvement of casting mechanical properties. The traditional grain size prediction method cannot give consideration to both the efficiency and accuracy because of its inherent defects. To improve the efficiency and accuracy of predicting grain size for aluminum alloy die castings, this study proposes a prediction method that is based on the genetic algorithm-extreme learning machine (GA-ELM) model. ELM has the characteristics of few parameter settings, fast learning and good generalization performance, but the algorithm randomly generates the initial input layer weight matrix and the hidden layer threshold matrix, which greatly affects the prediction result. By exploiting GA's excellent global optimization ability, the optimal initial input layer weight matrix and the hidden layer threshold matrix for ELM can be found. The establishment of GA-ELM model can considerably improve the prediction accuracy of ELM model. This study uses grain size as the output parameters and relevant die casting process parameters as the input parameters. The castings produced under different die-casting process parameters are obtained experimentally, and the microstructures of specified sections of key casting positions are analyzed and measured to obtain the average grain size of the sec tions, i.e. the output parameters. The GA-ELM model is trained and tested using these data. To verify the superiority of the GA-ELM model in grain size prediction, this study compares the prediction results of GA-ELM model with the GA-BP neural network model and the original ELM model, and eventually verifies the reliability of GA-ELM model prediction results through metallographic structure measurement experiment. The results show that the GA-ELM model has higher prediction accuracy than the GA-BP neural network model and the original ELM model. Besides, its prediction efficiency is higher than the GA-BP model, while is lower than the original ELM model. With fairly high prediction accuracy and efficiency, the GA-ELM model can meet the actual engineering requirements. Furthermore, its prediction reliability and excellent prediction effect are verified by the results of metallographic structure measurement experiment.
Fig.1 An algorithm flow of genetic algorithms-extreme learning machine (GA-ELM)
Fig.2 Geometric model of die casting of automobile air compressor end cover
No.
TP / ℃
TI / ℃
VS / (ms-1)
VF / (ms-1)
1
190
650
0.2
3
2
200
660
0.3
4
3
210
670
0.4
5
4
220
680
0.5
6
Table 1 Molding process parameters and level setting
Fig.3 Geometric model of the joint of automobile air compressor end cover
No.
TP / ℃
TI / ℃
VS / (ms-1)
VF / (ms-1)
D / μm
1
650
190
0.2
3
250
2
650
200
0.3
4
349
3
650
210
0.4
5
282
4
650
220
0.5
6
303
5
660
190
0.3
5
282
6
660
200
0.2
6
251
7
660
210
0.5
3
313
8
660
220
0.4
4
328
9
670
190
0.4
6
274
10
670
200
0.5
5
351
11
670
210
0.3
4
346
12
670
220
0.2
3
251
13
680
190
0.5
4
245
14
680
200
0.4
3
259
15
680
210
0.3
6
245
16
680
220
0.2
5
280
17
650
190
0.5
3
260
18
650
200
0.4
4
281
19
650
210
0.3
5
294
20
650
220
0.2
6
277
21
660
190
0.5
4
320
22
660
200
0.4
3
227
23
660
210
0.3
6
288
24
660
220
0.2
5
379
25
670
190
0.4
5
268
26
670
200
0.5
5
301
27
670
210
0.2
3
292
28
670
220
0.3
4
320
29
680
190
0.4
6
213
30
680
200
0.5
5
216
31
680
210
0.2
4
280
32
680
220
0.3
3
246
Table 2 Experimental results of process parameters of the training set
No.
Parameter
Setting
1
Number of ELM hidden layer nodes
32
2
Population size
60
3
Maximum iterations
150
4
Crossover possibility
0.8
5
Mutation probability
0.5
6
Objective function
The minimum of average relative errors
7
Fitness evaluation method
Linear evaluation
8
Value range of weights Wij
[-1, 1]
9
Value range of thresholds bi
[-1, 1]
10
Terminal condition
Maximum number of iterations
Table 3 GA-ELM parameters setting table
No.
TP / ℃
TI / ℃
VS / (ms-1)
VF / (ms-1)
D / μm
1
650
200
0.4
6
271
2
650
210
0.5
5
308
3
650
210
0.5
6
258
4
660
200
0.3
4
270
5
660
210
0.4
3
235
6
670
190
0.5
5
262
Table 4 Experimental results of process parameters of the test set
Fig.4 Evolution consequences of the GA optimization
Fig.5 Comparisons between predicted results of GA-ELM model and output true values of test set
Fig.6 Comparisons between predicted results of GA-BP model and output true values of test set
Fig.7 Comparisons between predicted results of ELM model and output true values of test set
Model index
Ea / μm
Er / %
t / s
GA-ELM
10.2
3.8
47.03
GA-BP
14.4
5.5
107.02
ELM
22.8
8.6
2.23
Table 5 Comparisons of prediction consequences for the three models
Fig.8 Metallographic microstructures of different positions (a~c) on the same cross-section of the joint of automobile air compressor end cover
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