Researching | Optimization of Laser Cleaning Process Parameters for Petroleum Pipe Threads Based on Response Surface Method and Particle Swarm Algorithm

Journals >Chinese Journal of Lasers >Volume 49 >Issue 22 >Page 2202001 > Article

Chinese Journal of Lasers
Vol. 49, Issue 22, 2202001 (2022)

Optimization of Laser Cleaning Process Parameters for Petroleum Pipe Threads Based on Response Surface Method and Particle Swarm Algorithm

Xingwei Sun^1、2, Zhong Zhang^1、2, Heran Yang^1、2、*, Zhixu Dong^1、2, and Yin Liu^1、2

Author Affiliations

¹School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, Liaoning, China

²Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang 110870, Liaoning, China

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DOI: 10.3788/CJL202249.2202001 Cite this Article Set citation alerts

Xingwei Sun, Zhong Zhang, Heran Yang, Zhixu Dong, Yin Liu. Optimization of Laser Cleaning Process Parameters for Petroleum Pipe Threads Based on Response Surface Method and Particle Swarm Algorithm[J]. Chinese Journal of Lasers, 2022, 49(22): 2202001 Copy Citation Text

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Laser cleaning platform. (a) Principle diagram of laser cleaning; (b) experimental equipment for laser cleaning; (c) scanning electron microscope; (d) confocal laser microscope; (e) ultra-depth-of-field microscope; (f) laser cleaning test bench for pipe thread

Fig. 1. Laser cleaning platform. (a) Principle diagram of laser cleaning; (b) experimental equipment for laser cleaning; (c) scanning electron microscope; (d) confocal laser microscope; (e) ultra-depth-of-field microscope; (f) laser cleaning test bench for pipe thread

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Surface morphologies of rust layer. (a) Macro-morphology of petroleum pipe thread; (b) micro-morphology of peeling layer easy to fall off in rust layer; (c) micro-morphology of dense layer; (d) element content distribution of rust layer

Fig. 2. Surface morphologies of rust layer. (a) Macro-morphology of petroleum pipe thread; (b) micro-morphology of peeling layer easy to fall off in rust layer; (c) micro-morphology of dense layer; (d) element content distribution of rust layer

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Fig. 3. Thread surface after laser cleaning. (a) Overall morphology; (b) local amplification at A′

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Fig. 4. Sample after wire cutting

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Fig. 5. Micro-morphologies under different laser powers. (a) 600 W; (b) 550 W; (c) 500 W; (d) 450 W; (e) 400 W

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Fig. 6. Surface roughnesses under different laser powers

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Fig. 7. Contents of oxygen elements under different laser powers

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Fig. 8. Micro-morphologies under different defocusing amounts. (a) +4 mm; (b) +3 mm; (c) +2 mm; (d) +1 mm; (e) 0 mm

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Fig. 9. Surface roughnesses under different defocusing amounts

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Fig. 10. Contents of oxygen elements under different defocusing amounts

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Fig. 11. Micro-morphologies under different scanning speeds. (a) 3000 mm/s; (b) 2500 mm/s; (c) 2000 mm/s; (d) 1500 mm/s; (e) 1000 mm/s

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Fig. 12. Surface roughnesses under different scanning speeds

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Fig. 13. Contents of oxygen elements under different scanning speeds

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Fig. 14. Residual plot of mathematical model

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Fig. 15. Flow chart of improved particle swarm algorithm

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Fig. 16. Convergence curves of two algorithms

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Fig. 17. Contents of elements after cleaning under optimized process parameters

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Fig. 18. Molten pool morphology after cleaning under optimized process parameters

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Parameter	Value
Wavelength /nm	1064
Laser power /W	≤1000
Pulse width /ns	100
Frequency /kHz	≤100
Scanning speed /(mm·s^－1)	≤3000
Spot diameter /mm	0.05

Table 1. Main parameters of pulsed lasers

Level	Laser power /W	Scanning speed /(mm·s^－1)	Defocusing amount /mm
Level 1	400	3000	+4
Level 2	450	2500	+3
Level 3	500	2000	+2
Level 4	550	1500	+1
Level 5	600	1000	0

Table 2. Orthogonal test level and parameters

Experiment No.	Laser power /W	Scanning speed /(mm·s^－1)	Defocusing amount /mm	Surface roughness /μm
1	600	3000	+4	9.13
2	600	2500	+3	8.84
3	600	2000	+2	8.23
4	600	1500	+1	9.27
5	600	1000	0	11.82
6	550	3000	+3	8.21
7	550	2500	+2	7.62
8	550	2000	+1	8.13
9	550	1500	0	8.44
10	550	1000	+4	7.42
11	500	3000	+2	6.53
12	500	2500	+1	7.14
13	500	2000	0	7.37
14	500	1500	+4	8.83
15	500	1000	+3	5.44
16	450	3000	+1	8.25
17	450	2500	0	7.67
18	450	2000	+4	7.39
19	450	1500	+3	6.64
20	450	1000	+2	6.92
21	400	3000	+1	7.81
22	400	2500	+4	8.27
23	400	2000	+3	7.23
24	400	1500	+2	6.44
25	400	1000	0	10.41

Table 3. Experimental data

Level	Laser power	Scanning speed	Defocusing amount
Level 1	9.458	7.986	8.208
Level 2	7.964	7.908	7.388
Level 3	7.062	7.670	7.148
Level 4	7.374	7.924	8.120
Level 5	8.032	8.402	9.142
Range	2.396	0.732	1.994

Table 4. Range analysis table

Experiment No.	Laser parameter			Surface roughness /μm
Experiment No.	Laser power /W	Defocusing amount /mm	Scanning speed /(mm·s^－1)	Surface roughness /μm
1	500	2	2000	5.77
2	450	2	1500	6.26
3	500	2	2000	6.36
4	450	3	2000	5.14
5	500	2	2000	5.82
6	500	2	2000	5.44
7	450	2	2500	5.61
8	500	1	1500	6.14
9	550	2	1500	6.38
10	500	1	2500	7.14
11	500	3	2500	5.25
12	550	2	2500	7.62
13	500	3	1500	4.85
14	450	1	2000	6.75
15	550	1	2000	8.13
16	500	2	2000	5.74
17	550	3	2000	6.65

Table 5. Experimental design matrix

Source	Sum of squares	Degree of freedom	Mean square	F value	P value	Reliability
Model	11.90	9	1.32	15.43	0.0008	Significant
A	3.15	1	3.15	36.75	0.0005
B	4.91	1	4.91	27.34	0.0001
C	0.50	1	0.50	5.78	0.0472
AB	4.225×10^－3	1	4.225×10^－3	0.049	0.8306
AC	0.89	1	0.89	10.42	0.0145
BC	0.09	1	0.09	1.05	0.3396
A²	2.26	1	2.26	26.32	0.0014
B²	0.05	1	0.05	0.59	0.4679
C²	0.034	1	0.034	0.40	0.5460
Residual	0.60	7	0.086
Lack of fit value	0.16	3	0.052	0.47	0.7347	Not significant
Pure error	0.44	4	0.11
Total	12.50	16
$R_{PRED}^{2}$ ＝0.7458		$R_{ADJ}^{2}$ ＝0.8903
R＝13.734

Table 6. Analysis of model variance

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Xingwei Sun, Zhong Zhang, Heran Yang, Zhixu Dong, Yin Liu. Optimization of Laser Cleaning Process Parameters for Petroleum Pipe Threads Based on Response Surface Method and Particle Swarm Algorithm[J]. Chinese Journal of Lasers, 2022, 49(22): 2202001

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