Laser Paint Removal Process Parameter Optimization via Response Surface Methodology

Jianian Yang; Jianzhong Zhou; Qi Sun; Xiankai Meng; Ming Zhu; Zhaoheng Guo; Qiang Fu

doi:10.3788/LOP56.231402

Laser & Optoelectronics Progress, Volume. 56, Issue 23, 231402(2019)

Laser Paint Removal Process Parameter Optimization via Response Surface Methodology

Jianian Yang^1、**, Jianzhong Zhou^1、*, Qi Sun¹, Xiankai Meng¹, Ming Zhu¹, Zhaoheng Guo¹, and Qiang Fu²

Author Affiliations

¹School of Mechanical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China

²Nanjing Institute of Advanced Laser Technology, Nanjing, Jiangsu 210038, China

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Abstract Get PDF(in Chinese)

Figures & Tables(17)

Fig. 1. Diagram of laser paint removal

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Fig. 2. Effect of interaction of all factors on surface composition

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Fig. 3. Cleaning surface micromorphology and elemental composition under different overlap rates. (a) 50%; (b) 75%

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Fig. 4. Effects of spot overlap rate and power on surface composition. (a) Contour graph; (b) response graph

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Fig. 5. Effects of spot overlap rate and number of scans on surface composition. (a) Contour graph; (b) response graph

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Fig. 6. Effect of interaction of all factors on surface roughness

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Fig. 7. Cleaning surface micromorphology and line roughness at different powers. (a) 20 W; (b) 25 W

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Fig. 8. Effects of power and number of scans on surface roughness. (a) Contour graph; (b) response graph

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Fig. 9. Effects of power and spot overlap rate on surface roughness. (a) Contour graph; (b) response graph

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Table 1. Main chemical composition of 304 stainless steel
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Table 1. Main chemical composition of 304 stainless steel
Element C Si Mn Cr Ni S P N Fe
Mass fraction /% ≤0.08 ≤1.0 ≤2.0 18.0-20.0 8.0-10.5 ≤0.03 ≤0.035 ≤0.1 Bal.

Table 2. Main technical parameters of laser paint removal system
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Table 2. Main technical parameters of laser paint removal system
Parameter Value
Wavelength /nm 1064
Power /W ≤ 100
Pulse width /ns 100
Frequency /kHz 10-100
Scan speed /(mm·s^-1) ≤8000
Focal length /mm 160
Waist diameter /mm 0.05

Table 3. Experimental factors and level design
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Table 3. Experimental factors and level design
Factor Extreme value
Low(-1) Medium(0) High(+1)
Power P /W 15 20 25
Spot overlap rate γ /% 25 50 75
Number of scans N 2 3 4

Table 4. Design matrix and experimental results

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Table 4. Design matrix and experimental results

No.	Parameter			Result
No.	P /W	γ /%	N	S	S_a /μm
1	25	25	3	50	0.6966
2	25	50	2	60	0.8036
3	25	75	3	20	1.1528
4	25	50	4	30	1.5258
5	15	50	2	40	1.5620
6	20	50	3	85	0.5980
7	20	50	3	85	0.6330
8	15	50	4	85	1.0620
9	20	50	3	80	0.7070
10	20	75	4	10	1.0588
11	20	50	3	80	0.7860
12	20	75	2	20	1.0844
13	20	50	3	75	0.5900
14	20	25	2	30	0.8038
15	20	25	4	65	0.8788
16	15	25	3	40	0.9316
17	15	75	3	60	0.9498

Table 5. ANOVA for surface composition model

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Table 5. ANOVA for surface composition model

Source	Sum of squares	Degree of freedom	Mean square	F	Probability
Model	10321.99	9	1146.89	16.62	0.0006
P	528.13	1	528.13	7.65	0.0278
γ	703.13	1	703.13	10.19	0.0152
Pγ	625.00	1	625.00	9.06	0.0197
PN	1406.25	1	1406.25	20.38	0.0027
γN	506.25	1	506.25	7.34	0.0303
γ²	3968.38	1	3968.38	57.52	0.0001
N²	1592.85	1	1592.85	23.09	0.0020
Lack of fit	393.75	3	131.25	5.89	0.0599
Note: residual-square R²=0.9553; adjusted residual-square $\begin{matrix} R_{Adj}^{2} \end{matrix}$ =0.8978; predicted residual-square $\begin{matrix} R_{pred}^{2} \end{matrix}$ =0.4040; adeq precision A_P=11.479.

Table 6. ANOVA for surface roughness model

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Table 6. ANOVA for surface roughness model

Source	Sum of squares	Degree of freedom	Mean square	F	Probability
Model	1.30	9	0.14	25.26	0.0002
γ	0.11	1	0.11	19.17	0.0032
Pγ	0.048	1	0.048	8.42	0.0230
PN	0.37	1	0.37	65.52	<0.0001
P²	0.32	1	0.32	56.24	0.0001
N²	0.38	1	0.38	66.34	<0.0001
Lack of fit	0.012	3	0.004	0.60	0.6484
Note: R²=0.9701, $\begin{matrix} R_{Adj}^{2} \end{matrix}$ =0.9317, $\begin{matrix} R_{pred}^{2} \end{matrix}$ =0.8196, A_P=15.336.

Table 7. Optimization criteria and weight
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Table 7. Optimization criteria and weight
Name Criteria Weight
Goal Lower Upper
P In range 15 25 1
γ In range 25 75 1
N Equal to 3 2 4 1
S Maximize 75 100 1
S_a Minimize 0 1 1

Table 8. Optimization results
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Table 8. Optimization results
Number P /W γ /% N S S_a /μm
1 19.18 46.06 3 82.9 0.661

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Jianian Yang, Jianzhong Zhou, Qi Sun, Xiankai Meng, Ming Zhu, Zhaoheng Guo, Qiang Fu. Laser Paint Removal Process Parameter Optimization via Response Surface Methodology[J]. Laser & Optoelectronics Progress, 2019, 56(23): 231402

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