Prediction of geometrical shape of coaxial wire feeding cladding in three-beam

Weiwei Jiang; Geyan Fu; Jiping Zhang; Shaoshan Ji; Shihong Shi; Fan Liu

doi:10.3788/IRLA202049.0305005

Journals >Infrared and Laser Engineering >Volume 49 >Issue 3 >Page 0305005 > Article

Infrared and Laser Engineering
Vol. 49, Issue 3, 0305005 (2020)

Prediction of geometrical shape of coaxial wire feeding cladding in three-beam

Weiwei Jiang, Geyan Fu^*, Jiping Zhang, Shaoshan Ji, Shihong Shi, and Fan Liu

Author Affiliations

School of Mechanical and Electric Engineering, Soochow University, Suzhou 215021, China

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DOI: 10.3788/IRLA202049.0305005 Cite this Article

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 Copy Citation Text

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Fig. 1. " Three-beam" internal wire feeding system

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Fig. 2. Section morphology of cladding layer

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Fig. 3. BP neural network topology diagram of Coaxial wire feeding clad

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Fig. 4. Adjustment parameter flow chart

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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)

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Composition	C	Mn	Si	P	S	Cu	Fe
Mass fraction	0.07%	1.53%	0.85%	0.011%	0.01%	0.12%	Bal.

Table 1. Composition of FRN-ER50-6 welding wire

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Experiment	Fixed parameters			Varied parameter	Craft window
Laser power single factor experiment	Scanning velocity (V_s)	Wire feeding velocity (V_f)	Defocus(D)	Power(P)	[1 300, 1 700 W]
Laser power single factor experiment	5 mm/s	14 mm/s	−2 mm	800 −1 800 W	[1 300, 1 700 W]
Scanning velocity single factor experiment	Power (P)	Wire feeding velocity(V_f)	Defocus (D)	Scanning velocity (V_s)	[3, 7 mm/s]
Scanning velocity single factor experiment	1 500 W	14 mm/s	−2 mm	2 −10 mm/s	[3, 7 mm/s]

Wire feeding velocity single factor experiment	Power (P)	Scanning velocity (V_s)	Defocus (D)	Wire feeding velocity(V_f)	[9, 15 mm/s]
Wire feeding velocity single factor experiment	1 500 W	5 mm/s	−2 mm	8 −20 mm/s	[9, 15 mm/s]
Defocus single factor experiment	Power (P)	Scanning velocity (V_s)	Wire feeding velocity (V_f)	Defocus (D)	[−2.5, −1.5 mm/s]
Defocus single factor experiment	1 500 W	5 mm/s	14 mm/s	−5 −1 mm	[−2.5, −1.5 mm/s]

Table 2. Single factor experiment table

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Index	P/W	V_s/mm•s⁻¹	V_f/mm•s⁻¹	D/mm	W/mm	H/mm	A/mm²	Cladding 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 3. Process parameters and experimental results of each deposition single-track

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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 4. Neural network parameter table of cladding width, height and cross-sectional area

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Method	Predictive variable	ACC_85%	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

Table 5. Comparison of prediction ability between quadratic regression model and BP neural network model

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