Infrared and Laser Engineering, Volume. 49, Issue 3, 0305005(2020)
Prediction of geometrical shape of coaxial wire feeding cladding in three-beam
Figures & Tables(10)
Fig. 3. BP neural network topology diagram of Coaxial wire feeding clad
Fig. 5. Comparison of cladding width actual value and predicted value(a), comparison of cladding height actual value and predicted value and area actual value (b), comparison of cladding predicted value(c)
Table 1. Composition of FRN-ER50-6 welding wire
View tableView in ArticleTable 1. Composition of FRN-ER50-6 welding wire
Composition C Mn Si P S Cu Fe Mass fraction 0.07% 1.53% 0.85% 0.011% 0.01% 0.12% Bal.
Table 2. Single factor experiment table
View tableView in ArticleTable 2. Single factor experiment table
Experiment Fixed parameters Varied parameter Craft window Laser power single factor experiment Scanning velocity ( Vs )Wire feeding velocity ( Vf )Defocus( D )Power( P )[1 300, 1 700 W] 5 mm/s 14 mm/s −2 mm 800 −1 800 W Scanning velocity single factor experiment Power ( P )Wire feeding velocity( Vf )Defocus ( D )Scanning velocity ( Vs )[3, 7 mm/s] 1 500 W 14 mm/s −2 mm 2 −10 mm/s Wire feeding velocity single factor experiment Power ( P )Scanning velocity ( Vs )Defocus ( D )Wire feeding velocity( Vf )[9, 15 mm/s] 1 500 W 5 mm/s −2 mm 8 −20 mm/s Defocus single factor experiment Power ( P )Scanning velocity ( Vs )Wire feeding velocity ( Vf )Defocus ( D )[−2.5, −1.5 mm/s] 1 500 W 5 mm/s 14 mm/s −5 −1 mm
Table 3. Process parameters and experimental results of each deposition single-track
View tableView in ArticleTable 3. Process parameters and experimental results of each deposition single-track
Index P/ WVs/ mm•s−1Vf/ mm•s−1D/ mmW/ mmH/ mmA/ mm2Cladding layer morphology 1 1 300.00 4.00 10.00 −2.50 3.45 1.14 2.81 
2 1 700.00 4.00 13.50 −1.75 3.85 1.14 2.99 
3 1 659.00 6.60 13.97 −2.32 3.64 0.68 1.67 
4 1 300.00 5.50 10.00 −2.50 3.37 0.80 1.93 
5 1 476.00 4.97 13.57 −1.55 3.31 1.17 2.78 
6 1 500.00 6.00 11.50 −2.50 3.63 0.82 2.09 
7 1 512.00 5.02 14.31 −1.83 3.20 1.10 2.56 
8 1 500.00 6.00 15.16 −2.50 3.33 1.10 2.50 
9 1 493.00 6.21 12.43 −1.66 3.09 0.79 1.71 
10 1 545.00 4.71 11.04 −2.17 3.40 1.06 2.56 
11 1 500.00 6.00 14.63 −2.50 3.36 0.94 2.09 
12 1 658.00 3.92 11.04 −1.87 3.54 1.17 3.02 
13 1 500.00 6.00 16.80 −2.50 3.31 1.21 2.74 
14 1 700.00 6.00 10.50 −2.00 3.73 0.68 1.72 
15 1 586.00 5.87 11.73 −2.15 3.84 0.83 2.19 
16 1 523.00 3.06 11.73 −1.83 3.62 1.45 3.97 
17 1 530.00 5.76 12.43 −2.09 3.30 0.85 1.95 
18 1 398.00 3.26 11.04 −1.61 3.38 1.37 3.40 
19 1 448.00 5.86 11.73 −2.04 3.13 0.88 1.95 
20 1 500.00 6.00 18.20 −2.50 3.24 1.34 2.93 
21 1 544.00 3.48 12.06 −2.07 3.35 1.32 3.25 
22 1 372.00 4.95 8.38 −2.02 3.92 0.71 1.93 
23 1 597.00 6.24 14.31 −2.11 3.38 0.96 2.33 
24 1 343.00 3.03 9.86 −1.98 3.50 1.23 3.14 
25 1 500.00 6.00 15.00 −1.50 3.44 1.02 2.49 
26 1 598.00 4.70 12.06 −2.29 3.58 1.04 2.63 
27 1 390.00 5.50 13.97 −1.95 3.10 1.01 2.23 
28 1 300.00 7.00 10.00 −2.50 3.31 0.63 1.49 
29 1 605.00 5.33 14.31 −1.54 3.08 1.04 2.32 
30 1 687.00 5.52 12.43 −1.51 3.35 0.88 2.08 
31 1 300.00 3.00 10.00 −2.50 3.59 1.32 3.54 
32 1 600.00 5.00 10.50 −2.25 3.59 0.85 2.10 
33 1 647.00 3.89 13.97 −2.22 3.54 1.31 3.23 
34 1 634.00 6.69 13.57 −1.94 3.42 0.87 2.06 
35 1 500.00 6.00 9.70 −2.50 3.51 0.69 1.71 
36 1 500.00 6.00 16.80 −2.50 3.39 1.07 2.41 
37 1 500.00 6.00 10.73 −2.50 3.74 0.74 1.92 
38 1 523.00 3.06 11.73 −1.83 3.27 1.44 3.59 
39 1 700.00 6.00 10.50 −2.00 3.53 0.70 1.70 
40 1 600.00 4.00 12.00 −1.50 3.94 0.95 2.61 
41 1 661.00 4.26 12.06 −1.56 3.54 1.10 2.91 
42 1 500.00 4.00 10.50 −2.50 3.73 0.73 1.86 
43 1 300.00 5.00 13.50 −1.50 3.48 0.99 2.37 
44 1 559.00 3.28 12.43 −1.60 3.42 1.34 3.38 
45 1 300.00 5.00 10.00 −2.50 3.26 0.93 2.18 
46 1 300.00 6.00 10.00 −2.50 3.42 0.74 1.83 
47 1 693.00 6.36 11.73 −2.18 3.34 0.82 1.90 
48 1 500.00 6.00 13.00 −2.50 3.64 0.74 1.90 
49 1 600.00 6.00 9.00 −1.75 3.26 0.62 1.42 
50 1 375.00 4.22 12.43 −2.05 3.18 1.15 2.64 
51 1 511.00 4.75 14.31 −1.72 3.27 1.22 2.87 
52 1 500.00 6.00 10.00 −2.50 3.60 0.76 1.90 
53 1 400.00 3.00 10.50 −1.50 3.48 1.27 3.22 
54 1 397.00 3.54 9.09 −2.40 3.34 1.00 2.35 
55 1 400.00 4.00 9.00 −2.25 3.52 0.85 2.10 
56 1 500.00 6.00 17.47 −2.50 3.33 1.12 2.57 
57 1 500.00 6.00 9.00 −2.50 3.27 0.69 1.61 
58 1 500.00 6.00 14.63 −2.50 3.42 0.88 2.15 
59 1 316.00 5.64 12.43 −2.41 3.44 0.87 2.10 
60 1 649.00 3.71 9.86 −2.05 3.45 1.00 2.45 
61 1 474.00 6.80 13.97 −2.28 3.44 0.64 1.48 
62 1 500.00 6.00 13.00 −2.50 3.55 0.88 2.11 
63 1 554.00 4.24 9.86 −1.68 3.49 0.97 2.38 
64 1 565.00 6.64 13.57 −1.83 3.02 0.95 2.01 
65 1 607.00 4.63 11.73 −2.17 3.27 1.08 2.52 
66 1 500.00 3.00 12.00 −1.50 4.10 1.17 3.35 
67 1 300.00 3.00 9.00 −2.50 3.65 1.01 2.55 
68 1 622.00 6.71 14.31 −1.84 3.23 0.90 2.00 
69 1 700.00 4.00 13.50 −1.75 4.03 1.10 3.13 
70 1 639.00 4.26 12.06 −1.93 3.50 1.08 2.69 
71 1 652.00 5.25 13.57 −1.68 3.45 1.07 2.64 
72 1 300.00 6.50 10.00 −2.50 3.59 0.70 1.74 
73 1 606.00 4.40 12.06 −2.07 3.62 1.07 2.70 
74 1 423.00 4.32 11.04 −2.32 3.45 1.04 2.53 
75 1 400.00 5.00 15.00 −1.75 3.53 1.08 2.66 
76 1 488.00 4.13 11.04 −2.40 3.48 0.95 2.23 
77 1 600.00 4.00 12.00 −1.50 3.96 0.85 2.38 
78 1 500.00 4.00 10.50 −2.50 3.79 0.79 2.10 
79 1 395.00 6.67 13.57 −1.83 3.15 0.82 1.77 
80 1 300.00 3.50 10.00 −2.50 3.75 1.20 3.25 
Table 4. Neural network parameter table of cladding width, height and cross-sectional area
View tableView in ArticleTable 4. Neural network parameter table of cladding width, height and cross-sectional area
Network parameter Width-BP Height-BP Cross-sectional area-BP Learning rate 0.5 0.1 0.1 Maxium number of iterations 1 000 5 000 5 000 Training target error 0.01 0.01 0.01 The number of hidden neurous 3 4 4 Nodes of each hidden neurous
Table 5. Comparison of prediction ability between quadratic regression model and BP neural network model
View tableView in ArticleTable 5. Comparison of prediction ability between quadratic regression model and BP neural network model
Method Predictive variable ACC85% RMSE Quadratic regression model Cladding width 100.00% 0.21 Cladding height 66.67% 0.13 Cladding area 73.33% 0.28 BP network model Cladding width 100.00% 0.21 Cladding height 100.00% 0.07 Cladding area 93.33% 0.24
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Weiwei Jiang, Geyan Fu, Jiping Zhang, Shaoshan Ji, Shihong Shi, Fan Liu. Prediction of geometrical shape of coaxial wire feeding cladding in three-beam[J]. Infrared and Laser Engineering, 2020, 49(3): 0305005
Paper Information
Received: Nov. 5, 2019
Accepted: --
Published Online: Apr. 22, 2020
The Author Email: Fu Geyan (fugeyan@suda.edu.cn)
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