吉林大学学报(工学版) ›› 2012, Vol. 42 ›› Issue (01): 188-192.

• 论文 • 上一篇    下一篇

变参数下的空气喷枪涂层厚度分布建模

王国磊1, 陈恳1, 陈雁2, 朱丽3, 王力强3, 颜华3   

  1. 1. 清华大学 精密仪器与机械学系,北京 100084;
    2. 中国人民解放军后勤工程学院 军事供油工程系,重庆 400016;
    3. 成都飞机工业(集团)有限责任公司,成都 610091
  • 收稿日期:2010-06-12 出版日期:2012-01-01 发布日期:2012-01-01
  • 通讯作者: 陈恳(1954-),男,教授,博士生导师.研究方向:机器人技术.E-mail:kenchen@tsinghua.edu.cn E-mail:kenchen@tsinghua.edu.cn
  • 作者简介:王国磊(1982-),男,博士.研究方向:喷涂机器人.E-mail:wang_gl@mail.tsinghua.edu.cn
  • 基金资助:

    "863"国家高技术研究发展计划项目(2009AA043701);摩擦学国家重点实验室项目(SKLT09A03);国家自然科学基金项目(50975148,51005126).

Film thickness distribution model with variable parameters for air spray gun

WANG Guo-lei1, CHEN Ken1, CHEN Yan2, ZHU Li3, WANG Li-qiang3, YAN Hua3   

  1. 1. Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, China;
    2. Department of Petroleum Supply Engineering, Logistical Engineering University of PLA, Chongqing 400016, China;
    3. Chengdu Aircraft Industrial (Group) Co Ltd, Chengdu 610091, China
  • Received:2010-06-12 Online:2012-01-01 Published:2012-01-01

摘要:

首先分析了影响空气喷枪涂层厚度及喷涂机器人作业过程中的可调和常变因素,然后利用BP神经网络方法对平板直行喷涂实验获得的实验数据加以拟合,建立以喷涂距离、喷枪移动速度、喷枪流量和测量点与喷枪轴线距离作为输入的喷枪喷涂模型。与传统模型相比,该模型用相对少量的实验数据就可以预测不同喷涂距离、喷枪移动速度和喷枪流量下的涂层厚度分布。实验数据表明,该模型准确、有效。

关键词: 自动控制技术, 喷涂机器人, 空气喷枪, 涂层厚度分布, 喷枪喷涂模型, BP神经网络

Abstract:

The factors affecting the coating thickness and the adjustable and changeable parameters during the working process of the spray painting robot were classified and analyzed. The experimental data acquired by the straight spraying experiment on the flat surface were fitted using a BP neural network. A spray gun model was built using the spray distance, spray gun moving velocity, spray flow rate, and the distance from the measure point to the axis of spray gun as inputs. Compared with the traditional model, the proposed model can predict the distribution of the coating thickness under different spraying distances, spray gun moving velocities, and spray flow rates with the relatively few experimental data. The validation experiments show that the proposed model is accurate and effective.

Key words: automatic control technology, spray painting robot, air spray gun, film thickness distribution, spray gun spray model, BP neural network

中图分类号: 

  • TP242.2


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