刘雪梅,刘涛,杨连生,李爱平.平面喷涂漆膜厚度分布规律研究与搭接参数优化[J].表面技术,2018,47(9):116-125.
LIU Xue-mei,LIU Tao,YANG Lian-sheng,LI Ai-ping.Spray Painting Film Thickness Distribution on Panel and Optimization of Width of Paint Film Overlay[J].Surface Technology,2018,47(9):116-125
平面喷涂漆膜厚度分布规律研究与搭接参数优化
Spray Painting Film Thickness Distribution on Panel and Optimization of Width of Paint Film Overlay
投稿时间:2018-06-02  修订日期:2018-09-20
DOI:10.16490/j.cnki.issn.1001-3660.2018.09.016
中文关键词:  自动化喷涂  漆膜厚度分布  遗传算法  BP神经网络算法  喷涂搭接参数  NSGA-II算法
英文关键词:automatic spray painting  thickness distribution of paint film  genetic algorithm  BP algorithm  spraying lap width  NSGA-II algorithm
基金项目:国家“高档数控机床与基础制造装备”科技重大专项-04专项项目(2013ZX04012-071) ;上海市科学技术委员会科研计划项目(14111104400)
作者单位
刘雪梅 同济大学 机械与能源工程学院,上海 201804 
刘涛 同济大学 机械与能源工程学院,上海 201804 
杨连生 同济大学 机械与能源工程学院,上海 201804 
李爱平 同济大学 机械与能源工程学院,上海 201804 
AuthorInstitution
LIU Xue-mei School of Mechanical Engineering, Tongji University, Shanghai 201804, China 
LIU Tao School of Mechanical Engineering, Tongji University, Shanghai 201804, China 
YANG Lian-sheng School of Mechanical Engineering, Tongji University, Shanghai 201804, China 
LI Ai-ping School of Mechanical Engineering, Tongji University, Shanghai 201804, China 
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中文摘要:
      目的 实现大型船舶外板表面喷涂的自动化作业,开展表面漆膜厚度分布规律研究,优化平面喷涂轨迹,求出最佳喷涂搭接参数。方法 以喷嘴雾幅宽度、距离、喷枪移动速度三个喷涂工艺参数为因素进行正交实验,获得漆膜厚度分布数据,使用分布函数进行实验数据表征,运用遗传算法优化拟合分布模型的三个关键参数。基于BP神经网络算法建立漆膜厚度分布预测模型,并对预测结果进行合理性分析。在此基础上,研究每道漆膜之间的搭接规律,运用NSGA-II算法求解最优漆膜搭接宽度。结果 计算得到15组实验数据对应分布模型的Tmax、w及β,构建的漆膜厚度分布预测模型能准确预测在不同喷涂工艺参数下的漆膜厚度分布。以第16组实验为例,计算得到了最优的漆膜搭接宽度为41.76 cm。结论 建立了喷涂工艺参数和膜厚分布规律之间的映射模型,该模型可以准确预测不同工艺参数下的涂层厚度分布,应用该模型计算出的最优漆膜搭接宽度能获得厚度均匀的涂层,为实现船舶外板的自动化喷涂做好了前期准备。
英文摘要:
      The work aims to study the thickness distribution of paint film so as to achieve automatic spray painting on huge ship outer panel, optimize surface spraying trajectory and find out the best lap width of paint film. The orthogonal experiment was designed with 3 factors, including width of spray nozzle, distance and speed of spray gun to obtain the thickness distribution data of paint film. β distribution function was used to characterize experimental data, and Genetic Algorithm (GA) was applied to optimize 3 key parameters of the β distribution model. The thickness distribution prediction model of paint film was established based on BP (Back Propagation) neural network, and the prediction results were analyzed reasonably. The overlap rules of each paint film were studied and NSGA-II algorithm was used to solve the best lap width of paint film. The corresponding Tmax, w and β of the β distribution model were obtained for the 15 groups of painting experimental data. The prediction model could accurately predict the thickness distribution of paint film under different spraying parameters. For example, in the 16th group of painting experiment, the best lap width of paint film was 41.76 cm. The mapping model between the spraying process parameters and the paint film thickness distribution is obtained. The mapping model can accurately predict the coating thickness distribution under different process parameters. The best lap width of paint film calculated based on this model can obtain the coating with uniform thickness, thus making good preparations for the automatic spraying of ship outer panel.
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