3.1 Layer Design
3.1.1 Principle of Layer Design
In hot forging process, the billet is usually heated to a higher temperature, so the surface temperature of the die is usually much higher than the inner temperature. In addition, humps and pits exist on the cavity surface, the stress and temperature distribution have extreme values in these regions. The cavity surface is in direct contact with the billet, and the friction is quite intense. For example, according to the simulation results of the temperature and the equivalent stress for a typical ultra-large hot forging die in service, the surface area of die cavity has the highest temperature, so the surface area is prone to have wear, burning, collapse, peeling as shown in Fig. 4.
Therefore, a better design is that the cavity surface has higher high-temperature wear resistance, the die transition layer also has higher strength, and the die base has better toughness[17]. As shown in Fig. 5, a failure hot forging die is redesigned into a three-layer structure, and the automatic WAAM technology is used to remanufacture the failure die. The thickness of the transition layer and the strengthening layer are determined according to the numerical simulation results of temperature distribution and stress distribution of the hot forging die in service.
When the hot forging die is in service, the surface temperature of the cavity is above 800 , and the strength of Fe-based alloy is very low at such a high temperature. Because the high temperature area is mostly concentrated on the surface of the die[18], the thickness of the surface strengthening layer is very small, and only the NAM strategy can be used to achieve uniform additive manufacturing.
3.1.2 Material Design
According to the analysis in the previous section, the surface region of hot forging die needs high high-temperature wear resistance, which is usually found in Ni-based and Co-based alloy materials[19][20][21]. In order to ensure that the Ni-based alloy material can achieve the strengthening effect, the following alloy materials shown in Table 1 are designed, a high-temperature wear resistance experiment is designed to analyze the high-temperature performance of the material.
Table 1 Composition of welding wire materials in different layers(wt., %)
Materials
|
C
|
Si
|
Mn
|
Cr
|
Mo
|
W
|
V
|
Ni
|
Co
|
Fe
|
Fe-based
|
0.24
|
0.75
|
1.00
|
5.43
|
2.42
|
1.79
|
0.35
|
--
|
--
|
Bal.
|
Ni-based
|
0.035
|
0.36
|
2.65
|
15.88
|
1.33
|
--
|
--
|
Bal.
|
--
|
7.75
|
Co-based
|
0.22
|
1.00
|
1.03
|
27.51
|
2.76
|
0.13
|
--
|
2.36
|
Bal.
|
1.59
|
To reduce the remanufacturing cost and improve the service life of the die, the transition layer is made of Fe-based alloy with low price, and the strengthening layer is made of Co-based or Ni-based alloy with high high-temperature wear resistance. The strengthening layer is usually designed to be very thin, about 8-10mm, so the use of expensive Ni-based alloy materials has a little impact on the repair cost of die, while Co-based and Ni-based alloy materials can greatly increase the service life.
As shown in Fig.6, the ball-disc high-speed friction machine was used to study low-temperature and high-temperature wear resistance performance of the three materials. The specimen size used in the experiment was 50mm*5mm, and the same surface polishing was carried out on each specimen, and them the specimens were cleaned with alcohol. As the billet temperature is usually higher than 800 , the specimens of the three materials were rubbed at 25 , 300 and 800 for 60min. In addition, due to the heavy-load borne by the hot forging die in service, the load applied in the experiment was about 200N.
Figure 7 compares the worn tack width and depth of Fe-based, Co-based and Ni-based alloys. Compared with other two kinds of materials, Ni-based alloy has the largest wear volume at temperature of 25 ℃. The wear volume of Co-based alloy is similar to Fe-based alloy at temperature of 25 ℃. The wear volume of Ni-based alloy decreases and the wear volume of Co-based alloy is slightly less than Fe-based alloy at temperatures of 300 . The wear volume of Co-based alloy is significantly decreased when the experiment temperature is reached 800℃. The wear resistance of Co-based and Ni-based alloy increases significantly with the increase of temperature, while that of Fe-based alloy is opposite.
3.2 Welding Process Parameters
To obtain higher geometric precision of hot forging die in the process of WAAM, this section discusses the relationship between welding process parameters and the geometry size of weld bead.
First the welding experiment of Fe-based alloy is carried out, and secondly a prediction model based on BP artificial neural network and inverse system based on genetic algorithm are established so that the welding parameters can be obtained quickly. The welding process of Ni-based alloy and Co-based alloy can be quickly obtained by the same method. The welding parameters that affect the shape of the weld bead mainly include: welding voltage, wire feeding speed and welding speed. If the welding voltage is too low, the weld bead will be high in height and narrow in width. If the welding voltage is too high, the welding heat will be large in the welding process, the weld bead with large width and low height will be formed. Therefore, 24V-30V is a more reasonable voltage range for welding wire with diameter of 1.3mm. The experimental welding voltage is set as: 24V, 26V, 28V, 30V;Wire feeding speed is set as: 5000mm/min, 7000mm/min, 9000mm/min, 11000mm/min; The welding speed is set as: 500mm/min, 700mm/min, 900mm/min, 1100mm/min. A total of 64 experiments were conducted, and the experimental data are shown in Table 2.
Table2 Weld shapes under different welding process parameters
number
|
voltage (V)
|
Feeding speed (mm/min)
|
Welding speed(mm/min)
|
width(mm)
|
height(mm)
|
number
|
voltage (V)
|
Feeding speed (mm/min)
|
Welding speed(mm/min)
|
width(mm)
|
height(mm)
|
1
|
24
|
7000
|
500
|
5.52
|
3.57
|
33
|
28
|
7000
|
500
|
8.81
|
2.30
|
2
|
24
|
7000
|
700
|
4.74
|
3.03
|
34
|
28
|
7000
|
700
|
7.03
|
1.72
|
3
|
24
|
7000
|
900
|
3.65
|
2.59
|
35
|
28
|
7000
|
900
|
6.36
|
1.59
|
4
|
24
|
7000
|
1100
|
3.47
|
1.80
|
36
|
28
|
7000
|
1100
|
5.19
|
1.32
|
5
|
24
|
9000
|
500
|
5.88
|
3.62
|
37
|
28
|
9000
|
500
|
9.73
|
2.39
|
6
|
24
|
9000
|
700
|
5.37
|
3.09
|
38
|
28
|
9000
|
700
|
8.24
|
2.13
|
7
|
24
|
9000
|
900
|
5.10
|
2.74
|
39
|
28
|
9000
|
900
|
6.40
|
1.98
|
8
|
24
|
9000
|
1100
|
4.42
|
2.40
|
40
|
28
|
9000
|
1100
|
5.84
|
1.73
|
9
|
24
|
11000
|
500
|
7.29
|
3.87
|
41
|
28
|
11000
|
500
|
9.89
|
2.87
|
10
|
24
|
11000
|
700
|
6.26
|
3.11
|
42
|
28
|
11000
|
700
|
9.09
|
2.63
|
11
|
24
|
11000
|
900
|
5.28
|
2.89
|
43
|
28
|
11000
|
900
|
7.02
|
2.38
|
12
|
24
|
11000
|
1100
|
4.93
|
2.62
|
44
|
28
|
11000
|
1100
|
6.10
|
2.07
|
13
|
24
|
13000
|
500
|
7.74
|
4.06
|
45
|
28
|
13000
|
500
|
10.10
|
3.57
|
14
|
24
|
13000
|
700
|
6.53
|
3.57
|
46
|
28
|
13000
|
700
|
9.39
|
2.89
|
15
|
24
|
13000
|
900
|
5.53
|
3.07
|
47
|
28
|
13000
|
900
|
7.33
|
2.67
|
16
|
24
|
13000
|
1100
|
5.17
|
2.74
|
48
|
28
|
13000
|
1100
|
6.48
|
2.21
|
17
|
26
|
7000
|
500
|
6.94
|
2.56
|
49
|
30
|
7000
|
500
|
9.34
|
2.04
|
18
|
26
|
7000
|
700
|
5.51
|
1.96
|
50
|
30
|
7000
|
700
|
7.22
|
1.63
|
19
|
26
|
7000
|
900
|
4.91
|
1.70
|
51
|
30
|
7000
|
900
|
6.87
|
1.53
|
20
|
26
|
7000
|
1100
|
4.41
|
1.43
|
52
|
30
|
7000
|
1100
|
5.50
|
1.25
|
21
|
26
|
9000
|
500
|
7.92
|
2.67
|
53
|
30
|
9000
|
500
|
10.21
|
2.12
|
22
|
26
|
9000
|
700
|
6.65
|
2.21
|
54
|
30
|
9000
|
700
|
8.75
|
1.93
|
23
|
26
|
9000
|
900
|
5.88
|
2.06
|
55
|
30
|
9000
|
900
|
6.57
|
1.79
|
24
|
26
|
9000
|
1100
|
5.17
|
1.89
|
56
|
30
|
9000
|
1100
|
6.16
|
1.58
|
25
|
26
|
11000
|
500
|
8.87
|
3.36
|
57
|
30
|
11000
|
500
|
10.51
|
2.61
|
26
|
26
|
11000
|
700
|
7.58
|
2.81
|
58
|
30
|
11000
|
700
|
9.57
|
2.40
|
27
|
26
|
11000
|
900
|
6.64
|
2.46
|
59
|
30
|
11000
|
900
|
8.47
|
2.36
|
28
|
26
|
11000
|
1100
|
5.56
|
2.21
|
60
|
30
|
11000
|
1100
|
6.35
|
1.96
|
29
|
26
|
13000
|
500
|
9.02
|
3.98
|
61
|
30
|
13000
|
500
|
10.53
|
3.07
|
30
|
26
|
13000
|
700
|
8.74
|
3.44
|
62
|
30
|
13000
|
700
|
9.68
|
2.70
|
31
|
26
|
13000
|
900
|
6.98
|
3.03
|
63
|
30
|
13000
|
900
|
8.74
|
2.32
|
32
|
26
|
13000
|
1100
|
6.02
|
2.51
|
64
|
30
|
13000
|
1100
|
7.03
|
2.15
|
The data in Table 2 reveal the nonlinear relationship between the welding process parameters and the geometry parameters of weld bead. An artificial neural network which structure is shown in Fig. 8 are used to approximate the nonlinear relationship. There are three inputs and two outputs in the network, which contain a hidden layer.
The correlation coefficient of the test samples was 0.99615 and the mean square error was 0.0687, indicating that the BP neural network had strong generalization ability and could effectively predict the width and height of weld bead. With the accurate prediction model, the prediction model can be coupled with the optimization algorithm, so as to realize the inverse welding parameters.
As shown in Fig. 10, the target width and height are set first. The error between target parameters and prediction parameters calculated by BP network is used as the fitness by genetic algorithm. The smaller the fitness, the easier the individual to retain in the iterative process. With the increase of the iterative number, the error becomes smaller and smaller, and finally the welding process parameters corresponding to the target parameters of weld bead can be obtained.
In fact, WAAM process is a fusion process of multiple weld beads. The smaller the weld spacing is, the better the surface flatness will be, but the higher the height will be. The larger the weld spacing is, the worse the surface flatness will be, and the lower the average height will be. Generally, when the weld spacing is about 1/2 of the weld width, the surface flatness and the height are more appropriate. As shown in Fig. 11b, the cross section could be not completely flat after the overlap of multiple welds, as long as the surface flatness reaches the allowable error.