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

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

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Table 2. Single factor experiment table

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 3. Process parameters and experimental results of each deposition single-track

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Table 3. Process parameters and experimental results of each deposition single-track

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

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Table 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

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

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

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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

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Paper Information

Received: Nov. 5, 2019

Accepted: --

Published Online: Apr. 22, 2020

The Author Email: Fu Geyan (fugeyan@suda.edu.cn)

DOI:10.3788/IRLA202049.0305005

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

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

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

Table 2. Single factor experiment table

Table 2. Single factor experiment table

Table 3. Process parameters and experimental results of each deposition single-track

Table 3. Process parameters and experimental results of each deposition single-track

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

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

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

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

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