Acta Metallurgica Sinica (English Letters) ›› 2018, Vol. 31 ›› Issue (1): 82-96.DOI: 10.1007/s40195-017-0669-1
• Orginal Article • Previous Articles Next Articles
Zhao-Yang Jin1(), Nan-Nan Li1, Kai Yan1, Jing-Xin Chen1, Dong-Lai Wei2, Zhen-Shan Cui3
Received:
2017-07-22
Online:
2018-01-20
Published:
2018-02-08
Zhao-Yang Jin, Nan-Nan Li, Kai Yan, Jing-Xin Chen, Dong-Lai Wei, Zhen-Shan Cui. Controlling Flow Instability in Straight Spur Gear Forging Using Numerical Simulation and Response Surface Method[J]. Acta Metallurgica Sinica (English Letters), 2018, 31(1): 82-96.
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Module | Teeth number | Pressure angle (°) | Reference diameter (mm) | Addendum coefficient | Bore diameter (mm) |
---|---|---|---|---|---|
0.5812 | 15 | 14.5 | 8.7173 | 1.0 | 3.985 ± 0.01 |
Table 1 Geometric parameters of the straight spur gear
Module | Teeth number | Pressure angle (°) | Reference diameter (mm) | Addendum coefficient | Bore diameter (mm) |
---|---|---|---|---|---|
0.5812 | 15 | 14.5 | 8.7173 | 1.0 | 3.985 ± 0.01 |
Fig. 4 Designed die structure with divided flow mandrel and linkage demolding device. 1—thermocouple, 2—linkage, 3—billet, 4—toothed ejector, 5—lower bolster, 6—upper platen, 7—toothed punch, 8—cooling pipe, 9—heating rod, 10—female die, 11—spring, 12—lower platen
Fig. 5 Distribution of flow instability as forged at temperature of 320 °C and apparent average strain rate of 0.63 a simulated results, bexperimental results
Design variables | Levels | ||||||
---|---|---|---|---|---|---|---|
- 1 | - 1/2 | - 1/3 | 0 | 1/3 | 1/2 | 1 | |
T (°C) | 250 | 287.5 | 300 | 325 | 350 | 362.5 | 400 |
V (mm/s) | 0.10 | 0.33 | 0.40 | 0.55 | 0.70 | 0.78 | 1.00 |
Table 2 Design variables and their levels
Design variables | Levels | ||||||
---|---|---|---|---|---|---|---|
- 1 | - 1/2 | - 1/3 | 0 | 1/3 | 1/2 | 1 | |
T (°C) | 250 | 287.5 | 300 | 325 | 350 | 362.5 | 400 |
V (mm/s) | 0.10 | 0.33 | 0.40 | 0.55 | 0.70 | 0.78 | 1.00 |
No | Coded | Actual | Response variables | |||
---|---|---|---|---|---|---|
A | B | T (°C) | V (mm/s) | L 1 | L 2 | |
1 | 0.500 | 0.500 | 362.50 | 0.78 | 0.000 | 0.610 |
2 | 0.000 | 0.500 | 325.00 | 0.78 | 0.000 | 0.650 |
3 | 1.000 | 1.000 | 400.00 | 1.00 | 0.000 | 0.505 |
4 | - 0.333 | 1.000 | 300.00 | 1.00 | 0.000 | 1.000 |
5 | 1.000 | 0.000 | 400.00 | 0.55 | 0.000 | 0.615 |
6 | 0.000 | 0.000 | 325.00 | 0.55 | 0.455 | 0.925 |
7 | 0.000 | - 0.500 | 325.00 | 0.33 | 0.390 | 0.970 |
8 | - 1.000 | - 1.000 | 250.00 | 0.10 | 0.000 | 1.000 |
9 | 1.000 | - 1.000 | 400.00 | 0.10 | 0.000 | 0.955 |
10 | - 0.500 | 0.000 | 287.50 | 0.55 | 0.435 | 1.000 |
11 | - 0.333 | 0.333 | 300.00 | 0.70 | 0.460 | 1.000 |
12 | - 1.000 | 0.000 | 250.00 | 0.55 | 0.775 | 1.000 |
13 | - 0.333 | - 1.000 | 300.00 | 0.10 | 0.000 | 1.000 |
14 | 0.500 | 0.000 | 362.50 | 0.55 | 0.000 | 0.605 |
15 | 1.000 | 0.333 | 400.00 | 0.70 | 0.000 | 0.600 |
16 | - 0.500 | - 0.500 | 287.50 | 0.33 | 0.000 | 1.000 |
17 | - 0.333 | - 0.333 | 300.00 | 0.40 | 0.475 | 1.000 |
18 | - 1.000 | 0.333 | 250.00 | 0.70 | 0.775 | 1.000 |
19 | 0.000 | 1.000 | 325.00 | 1.00 | 0.435 | 0.740 |
20 | - 1.000 | - 0.333 | 250.00 | 0.40 | 0.685 | 1.000 |
21 | 0.333 | 0.333 | 350.00 | 0.70 | 0.000 | 0.605 |
22 | 1.000 | - 0.333 | 400.00 | 0.40 | 0.000 | 0.640 |
23 | 0.333 | - 1.000 | 350.00 | 0.10 | 0.000 | 0.960 |
24 | - 1.000 | 1.000 | 250.00 | 1.00 | 0.815 | 1.000 |
25 | 0.333 | 1.000 | 350.00 | 1.00 | 0.000 | 0.600 |
26 | - 0.500 | 0.500 | 287.50 | 0.78 | 0.435 | 1.000 |
27 | 0.333 | - 0.333 | 350.00 | 0.40 | 0.000 | 0.635 |
28 | 0.500 | - 0.500 | 362.50 | 0.33 | 0.000 | 0.655 |
29 | 0.000 | - 1.000 | 325.00 | 0.10 | 0.000 | 1.000 |
Table 3 Design variables and two simulated responses
No | Coded | Actual | Response variables | |||
---|---|---|---|---|---|---|
A | B | T (°C) | V (mm/s) | L 1 | L 2 | |
1 | 0.500 | 0.500 | 362.50 | 0.78 | 0.000 | 0.610 |
2 | 0.000 | 0.500 | 325.00 | 0.78 | 0.000 | 0.650 |
3 | 1.000 | 1.000 | 400.00 | 1.00 | 0.000 | 0.505 |
4 | - 0.333 | 1.000 | 300.00 | 1.00 | 0.000 | 1.000 |
5 | 1.000 | 0.000 | 400.00 | 0.55 | 0.000 | 0.615 |
6 | 0.000 | 0.000 | 325.00 | 0.55 | 0.455 | 0.925 |
7 | 0.000 | - 0.500 | 325.00 | 0.33 | 0.390 | 0.970 |
8 | - 1.000 | - 1.000 | 250.00 | 0.10 | 0.000 | 1.000 |
9 | 1.000 | - 1.000 | 400.00 | 0.10 | 0.000 | 0.955 |
10 | - 0.500 | 0.000 | 287.50 | 0.55 | 0.435 | 1.000 |
11 | - 0.333 | 0.333 | 300.00 | 0.70 | 0.460 | 1.000 |
12 | - 1.000 | 0.000 | 250.00 | 0.55 | 0.775 | 1.000 |
13 | - 0.333 | - 1.000 | 300.00 | 0.10 | 0.000 | 1.000 |
14 | 0.500 | 0.000 | 362.50 | 0.55 | 0.000 | 0.605 |
15 | 1.000 | 0.333 | 400.00 | 0.70 | 0.000 | 0.600 |
16 | - 0.500 | - 0.500 | 287.50 | 0.33 | 0.000 | 1.000 |
17 | - 0.333 | - 0.333 | 300.00 | 0.40 | 0.475 | 1.000 |
18 | - 1.000 | 0.333 | 250.00 | 0.70 | 0.775 | 1.000 |
19 | 0.000 | 1.000 | 325.00 | 1.00 | 0.435 | 0.740 |
20 | - 1.000 | - 0.333 | 250.00 | 0.40 | 0.685 | 1.000 |
21 | 0.333 | 0.333 | 350.00 | 0.70 | 0.000 | 0.605 |
22 | 1.000 | - 0.333 | 400.00 | 0.40 | 0.000 | 0.640 |
23 | 0.333 | - 1.000 | 350.00 | 0.10 | 0.000 | 0.960 |
24 | - 1.000 | 1.000 | 250.00 | 1.00 | 0.815 | 1.000 |
25 | 0.333 | 1.000 | 350.00 | 1.00 | 0.000 | 0.600 |
26 | - 0.500 | 0.500 | 287.50 | 0.78 | 0.435 | 1.000 |
27 | 0.333 | - 0.333 | 350.00 | 0.40 | 0.000 | 0.635 |
28 | 0.500 | - 0.500 | 362.50 | 0.33 | 0.000 | 0.655 |
29 | 0.000 | - 1.000 | 325.00 | 0.10 | 0.000 | 1.000 |
Source | Sum of squares | df | Mean square | F value | P value Prob. > F |
---|---|---|---|---|---|
Model | 0.91 | 7 | 0.13 | 35.31 | < 0.0001 |
A | 0.34 | 1 | 0.34 | 92.48 | < 0.0001 |
B | 0.13 | 1 | 0.13 | 35.66 | < 0.0001 |
AB | 0.069 | 1 | 0.069 | 18.70 | 0.0003 |
A 2 | 0.001042 | 1 | 0.001042 | 0.28 | 0.6004 |
B 2 | 0.021 | 1 | 0.021 | 5.76 | 0.0258 |
AB 2 | 0.012 | 1 | 0.012 | 3.19 | 0.0885 |
A 3 | 0.10 | 1 | 0.10 | 27.43 | < 0.0001 |
Residual | 0.077 | 21 | 0.003684 | ||
Cor total | 0.99 | 28 |
Table 4 ANOVA for reduced cubic response surface of L 2
Source | Sum of squares | df | Mean square | F value | P value Prob. > F |
---|---|---|---|---|---|
Model | 0.91 | 7 | 0.13 | 35.31 | < 0.0001 |
A | 0.34 | 1 | 0.34 | 92.48 | < 0.0001 |
B | 0.13 | 1 | 0.13 | 35.66 | < 0.0001 |
AB | 0.069 | 1 | 0.069 | 18.70 | 0.0003 |
A 2 | 0.001042 | 1 | 0.001042 | 0.28 | 0.6004 |
B 2 | 0.021 | 1 | 0.021 | 5.76 | 0.0258 |
AB 2 | 0.012 | 1 | 0.012 | 3.19 | 0.0885 |
A 3 | 0.10 | 1 | 0.10 | 27.43 | < 0.0001 |
Residual | 0.077 | 21 | 0.003684 | ||
Cor total | 0.99 | 28 |
Source | Sum of squares | df | Mean square | F value | P value Prob. > F |
---|---|---|---|---|---|
Model | 1.90 | 7 | 0.27 | 11.42 | < 0.0001 |
A | 1.03 | 1 | 1.03 | 43.26 | < 0.0001 |
B | 0.012 | 1 | 0.012 | 0.49 | 0.4913 |
AB | 0.17 | 1 | 0.17 | 7.34 | 0.0131 |
A 2 | 0.10 | 1 | 0.10 | 4.35 | 0.0493 |
B 2 | 0.15 | 1 | 0.15 | 6.33 | 0.0200 |
A 2 B | 0.053 | 1 | 0.053 | 2.22 | 0.1509 |
AB 2 | 0.11 | 1 | 0.11 | 4.83 | 0.0394 |
Residual | 0.50 | 21 | 0.024 | ||
Cor total | 2.40 | 28 |
Table 5 ANOVA for reduced cubic response surface of L 1
Source | Sum of squares | df | Mean square | F value | P value Prob. > F |
---|---|---|---|---|---|
Model | 1.90 | 7 | 0.27 | 11.42 | < 0.0001 |
A | 1.03 | 1 | 1.03 | 43.26 | < 0.0001 |
B | 0.012 | 1 | 0.012 | 0.49 | 0.4913 |
AB | 0.17 | 1 | 0.17 | 7.34 | 0.0131 |
A 2 | 0.10 | 1 | 0.10 | 4.35 | 0.0493 |
B 2 | 0.15 | 1 | 0.15 | 6.33 | 0.0200 |
A 2 B | 0.053 | 1 | 0.053 | 2.22 | 0.1509 |
AB 2 | 0.11 | 1 | 0.11 | 4.83 | 0.0394 |
Residual | 0.50 | 21 | 0.024 | ||
Cor total | 2.40 | 28 |
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