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基于响应曲面法的木材喷涂漆雾扩散角度与均匀度优化OA北大核心CSTPCD

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

中文摘要英文摘要

[目的]探究木材喷涂喷头结构参数对喷口流速v2、漆雾扩散角度γ和漆雾均匀性λ的影响,求解喷头最优结构参数,提高木材喷涂效率和喷涂效果.[方法]基于雾滴碰撞、聚集和积累理论初步探究v2、γ和λ的影响因素,将优化参数设为原始喷头的7项关键内部结构参数,包括内壁倒角β、三段管道内壁长度L1至L3和三段管道内壁直径d1至d3,利用Design Expert设计7因素3水平3指标的BBD响应曲面试验,探究各结构参数对v2、γ和λ的影响显著性.BBD试验各组结果由3部分仿真获得,采用k-ε模型进行喷头内部流场仿真、KHRT模型进行高压平口雾化仿真.应用Image J测定漆雾扩散角度,通过Python运用"雾滴撒点法"完成对每组雾化仿真结果的均匀性标定,利用多目标优化方法求解喷头最优结构参数,并通过仿真喷涂和实机喷涂验证其喷涂效果.[结果]BBD响应曲面试验结果表明,7项关键内部结构参数对指标的影响十分复杂,但3项指标v2、γ和λ回归显著(P≤0.0001),多目标优化下的理论最优漆雾扩散角度为21.28°、最优漆雾均匀性为3.053;最优喷头的喷涂仿真试验结果表明,优化后喷口流速v2由35.8 m·s-1升至107m·s-1,漆雾扩散角度γ由16.74°升至21.09°(与理论相差0.883%),漆雾均匀性λ由3.62升至3.03(λ越小代表越均匀,与理论相差0.751%).在木材喷涂实机试验中,优化前后木材喷涂试样漆厚标准差由21.71 μm降至17.74 μm,单面喷涂时间由6.2 s降至5.5 s,单面喷头移动行程由3 255 mm降至2 887 mm.[结论]基于"雾滴撒点法"的漆雾均匀性评判标准以及基于响应曲面法和多目标的喷头结构参数优化方法可对木材喷涂喷头优化设计提供一定参考,对提高木材喷涂漆面均匀性和木材喷涂效率具有一定效果和帮助.

[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 pm to 17.74 pm 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.

杨春梅;刘彤彬;马亚强;丁禹程;王金聪;胡松;宋文龙

东北林业大学机电工程学院 哈尔滨 150040东北林业大学计算机与控制工程学院 哈尔滨 150040广东博硕涂装技术有限公司 佛山 520308

林学

木材喷涂喷涂均匀性扩散角度雾化仿真响应曲面法多目标优化

wood sprayingspray uniformitydiffusion angleatomization simulationresponse surface method(RSM)multi-objective optimization

《林业科学》 2024 (006)

136-147 / 12

黑龙江省重点研发项目"基于5G被动式绿色建筑门窗材数字协同加工中心关键技术研究"(2022ZX01A17);佛山市顺德区科技创新项目"基于机器视觉的人工智能喷涂机器人关键技术与核心装备研发"(顺科发[2021]83).

10.11707/j.1001-7488.LYKX20220530

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