全液压驱动主轴水下钻孔建模分析与优化
详细信息    查看全文 | 推荐本文 |
  • 英文篇名:Modeling analysis and optimization of underwater drilling characteristics of full hydraulic drive spindle
  • 作者:祝宗祥 ; 张丽红 ; 王朝平 ; 祝笑梅
  • 英文作者:ZHU Zong-xiang;ZHANG Li-hong;WANG Chao-ping;ZHU Xiao-mei;China Petroleum Pipeline Administration Engineering Co., Ltd.;
  • 关键词:液压驱动 ; 钻孔 ; BP神经网络 ; 遗传算法
  • 英文关键词:hydraulic drive;;drilling;;BP neural network;;genetic algorithm
  • 中文刊名:焊接技术
  • 英文刊名:Welding Technology
  • 机构:中国石油管道局工程有限公司华油工建公司;
  • 出版日期:2019-06-28
  • 出版单位:焊接技术
  • 年:2019
  • 期:06
  • 基金:面向航空发动机缺损叶片自动焊接修复的三维测量与重构系统关键技术和方法研究(U1433117)
  • 语种:中文;
  • 页:6+87-91
  • 页数:6
  • CN:12-1070/TG
  • ISSN:1002-025X
  • 分类号:TE973
摘要
目前高速液压驱动主轴头在现场摩擦叠焊中,低速钻孔效率较低。通过分析,提出全液压驱动主轴水下钻孔特性建模方法和离线优化方案。根据数次试验获取钻孔数据参数,采用BP神经网络对数据进行拟合研究,获得水下钻孔时扭矩特性,经遗传算法寻优,得出优化切削参数,并将此作为在线优化初始输入值。该方法可获得全液压驱动主轴水下钻孔特性及最优切削参数,减少了现场钻孔在线优化时间,在较大程度上提高了加工效率。
        At present, the high speed hydraulic drive spindle head has low efficiency in low speed drilling in spot friction welding. Through analysis, the modeling method and off-line optimization of full hydraulic drive spindle underwater drilling characteristics were put forward. According to several tests to obtain drilling data parameters, BP neural network was used to fit the data, and torque characteristics of underwater drilling were obtained. Optimized cu tting parameters were obtained by genetic algorithm optimization, and this was used as the initial input value of online optimization. This method could obtain the underwater drilling characteristics and the optimal cutting parameters of the full hydraulic drive spindle, reduced the on-line drilling optimization time and greatly improve the machining efficiency.
引文
[1]陈永亮,彭涛,刘德帅,等.面向现场加工的全液压驱动主轴水下钻孔特性建模与优化方法[J].中国机械工程, 2018, 29(4):471-476.
    [2]宁重阳.全液压电液比例数控钻床的设计与研究[D].云南昆明:昆明理工大学, 2013.
    [3]晁晓圆.数控加工中心ATC装置的控制分析与故障排除[J].机床与液压, 2017, 45(2):160-162.
    [4]常晓宇,齐向阳,聂晓菊,等.基于液压预紧结构主轴单元的研究[J].机械设计与研究, 2017, 33(4):136-140.
    [5]曹军,杨帆,黄江中,等.液压驱动水下摩擦焊机研制[J].焊接, 2014(8):44-48.
    [6]宋庆胜.机床主轴负载转矩实时监控与实验研究[D].上海:东华大学, 2017.
    [7]张毅,姚锡凡.加工过程的智能控制方法现状及展望[J].组合机床与自动化加工技术, 2013(4):1-3.
    [8]陈炯.数控机床智能化的主要技术特征[J].机床与液压,2016, 44(2):73-76.