基于响应曲面法的木材喷涂漆雾扩散角度与均匀度优化

doi:10.11707/j.1001-7488.LYKX20220530

Abstract

Abstract:

Objective: The objective of this study is to investigate the impact of structural parameters of a wood spray nozzle on nozzle speed (v₂), paint mist diffusion angle (γ), and paint mist uniformity (λ), and to determine the optimal structural parameters for improving spraying efficiency and effectiveness in wood spraying. Method: Using the theory of droplet collision, aggregation, and accumulation, we examined the factors influencing nozzle speed (v₂), paint mist diffusion angle (γ), and paint mist uniformity (λ). The seven key internal structural parameters of the original nozzle, including inner wall chamfering angle (β), length of three pipelines (L₁?L₃), and diameters of three pipelines (d₁?d₃), were chosen as optimization parameters. A 7-factor, 3-level, 3-index Box-Behnken design (BBD) response surface test was designed using Design Expert software. The significance of each structural parameter on nozzle speed, paint mist diffusion angle, and paint mist uniformity was determined. The BBD test results were obtained through a three-part simulation: internal flow field simulation of the nozzle using the k-ε model, high-pressure flat mouth atomization simulation using the KHRT model, and determination of paint mist diffusion angle using Image J and Python were used to calibrate the uniformity of atomization simulation results using the“droplet spreading method”. The optimal structural parameters were obtained through multi-objective optimization, and the spraying effect was validated through simulation and actual spraying. Result: The BBD response surface test results showed that the influence of the seven key internal structural parameters on the indexes was complex, but the regression of nozzle speed (v₂), paint mist diffusion angle (γ), and paint mist uniformity (λ) was statistically significant (P≤0.000 1). The theoretical optimal paint spray diffusion angle under multi-objective optimization was determined to be 21.28°, and the optimal uniformity was 3.053. The simulation of the optimal spray nozzle showed an increase in nozzle speed from 35.8 m·s^?1 to 107 m·s^?1, paint mist diffusion angle (γ) from 16.74° to 21.09° (0.883% difference from theory), and paint mist uniformity (λ) from 3.62 to 3.03 (0.751% difference from theory). In the wood spraying test, the standard deviation of paint thickness on wood specimens was reduced from 21.71 μm to 17.74 μm after optimization. Additionally, the spraying time on one side decreased from 6.2 s to 5.5 s, and the travel distance of the spray nozzle on one side decreased from 3 255 mm to 2 887 mm. Conclusion: The “droplet spreading method” and the optimization of nozzle structure parameters using response surface methodology (RSM) and multi-objective techniques can provide guidance for the optimal design of wood spray nozzles. This approach effectively improves the uniformity and efficiency of wood spraying.

Key words: wood spraying, spray uniformity, diffusion angle, atomization simulation, response surface method (RSM), multi-objective optimization

CLC Number:

S776.03

Chunmei Yang,Tongbin Liu,Yaqiang Ma,Yucheng Ding,Jincong Wang,Song Hu,Wenlong Song. Optimization of Paint Diffusion Angle and Uniformity in Wood Spraying Using Response Surface Method[J]. Scientia Silvae Sinicae, 2024, 60(6): 136-147.

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水平Level	β/(°)	L₁/mm	L₂/mm	L₃/mm	d₁/mm	d₂/mm	d₃/mm
-1	30	6	1	1	4	1	0.1
0	45	9	3	2	5	2	0.2
1	60	12	5	3	6	3	0.3

黏度 Viscosity/(Pa·s)	密度 Density/(kg·100 L^?1)	张力 Tension/(N·m^?1)	推荐压力 Pressure/MPa	推荐稀释比例 Recommended dilution ratio
20	1.3	0.073	30	1∶1

黏性模型 Viscosity model	大气压力 Atmospheric pressure/Pa	管道进压 Pipeline inlet pressure/Pa	重力 Gravity/ (m·s^?2)	漆料黏度 Paint viscosity/(Pa·s)	漆料密度 Paint density/ (kg·m^?3)	全局网格 Global grid/mm	加密网格 Encrypted grid/mm	粗糙高度 Rough height/mm	粗糙常数 Roughness constant
k-ε	1.013×10⁵	3×10⁷	9.8	2.77	1 300	0.1	0.03	0.046	0.5

黏性模型 Viscosity model	破碎模型 Crushing model	喷口流速 Nozzle flow rate/(kg·s^?1)	喷头内径 Nozzle inner diameter/mm	孔板圆角 Orifice plate rounding/mm	孔板长度 Orifice plate length/mm	喷涂常数 Spraying constant	漆料张力 Tension/ (N·m^?1)	全局网格 Global grid/mm	喷涂距离 Spraying distance/mm
k-ε	KHRT	v₂	d₃	0.25	L₃	4.9	0.072 9	0.05	300

β/(°)	L₁/mm	L₂/mm	L₃/mm	d₁/mm	d₂/mm	d₃/mm
33.43	3.98	0.42	0.99	4.97	1.96	0.29

Optimization of Paint Diffusion Angle and Uniformity in Wood Spraying Using Response Surface Method

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