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导弹天线罩外廓形精密测量与修磨技术研究

Study on Precision Measurement and Grinding for Radome Outer Surface of Missile
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摘要 导弹打击目标的精确性很大程度上需依靠导弹表面天线罩的电厚度的精度,而影响天线罩电厚度精度的因素有很多,如天线罩壁厚、材料介电常数、电磁波入射角,波长等,研究表明可以通过只改变天线罩的壁厚以补偿电厚度的偏差。介绍了一种基于Keyence(基恩士)传感器LK系列的在线测量与修磨系统,详细阐述了系统的硬件结构,给出了系统软件的主要设计流程。最后对测量修磨实例进行了分析与评价,结果表明该系统完全满足天线罩几何厚度测量功能和修磨量精度的要求。同时系统还具有低成本,操作简单,使用方便和可靠性高的特点,具有良好的市场推广应用价值。 The accuracy of missile attacking target largely depends on radome’s electricity thickness on surface,and there are many factors which can influence the accuracy of electricity thickness,such as radome wall thickness,material dielectric constant,electromagnetic incident angle,the wavelengths and so on,research shows that it is possible only by changing radome wall thickness to compensate the deviation of electricity thickness.It introduces a kind of online measurement and grinding system based on the sensor lk-coriolis Keyence,and then elaborates the hardware structure in detail and presents the main software design process of the system.Finally,with an analysis having being made and evaluation based on the instance of measuring and grinding,the results show that the system can satisfy fully the function of radome geometric thickness measurement and the requirements of grinding quantity precision.At the same time,the system also has the characteristic of low cost,simple operation,easy to use and high reliability with good promotional application value in market.
出处 《机械设计与制造》 北大核心 2012年第6期80-82,共3页 Machinery Design & Manufacture
基金 上海市重点学科建设项目(B602) 国家863项目(2008AA04Z123)
关键词 天线罩 在线测量 电厚度 几何厚度 修磨量 Radom Online Measurement Electricity Thickness Geometric Thickness Grinding Quantity
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  • 1冯瑞,宋春林,张艳珠,邵惠鹤.基于支持向量机与RBF神经网络的软测量模型比较研究[J].上海交通大学学报,2003,37(z1):122-125. 被引量:16
  • 2Xu K,Xie M,Tang L C,et al.Applications of neural networks in forecasting engine systems reliabitity[J]Applied Soft Computing,2003 2(4) :255-268.
  • 3Vapnik V N.Statistical learning theory[M].New York:Wiley, 1998.
  • 4Smola A J,Scholkopf B.A tutorial on support vector regression, NC-TR-98-030[R].UK:Royal Holloway College University of London, 1998.
  • 5Burges C J C.A tutorial on support vector machines for pattern recognition[J].Knowledge Discovery. and Data Mining, 1998,2(2) : 121-167.
  • 6Thissen U,Van Brakel R,de Weijer A P,et al,Using support vector machines for time series predictiou[J].Chemometrics and Intelligent Laboratory Systems, 2003,69 : 35-49.
  • 7Kim Kyoung-jae.Financial time series forecasting using support vector machines[J].Neurocomputing,2003,55( 1-2):307-319.
  • 8Tay F E H,Cao L J.Application of support vector machines in financial time series forecasting[J].Omega,2001,29(4):309-317.
  • 9Chapelle O,Vapnik V,Bousquet O,et al.Choosing kernel parameters for support vector machines[J].Machine Learning, 2001,46( 1 ) : 131-160.
  • 10杜树新,吴铁军.用于回归估计的支持向量机方法[J].系统仿真学报,2003,15(11):1580-1585. 被引量:139

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