Thermal error modeling with dirty and small training sample for the motorized spindle of a precision boring machine
作者: Mohan LeiGedong JiangJun YangXuesong MeiPing XiaLiang Zhao
作者单位: 1Xi’an Jiaotong University
2Shanghai Jiao Tong University
刊名: The International Journal of Advanced Manufacturing Technology, 2017, Vol.93 (1-4), pp.571-586
来源数据库: Springer Nature Journal
DOI: 10.1007/s00170-017-0531-7
关键词: Motorized spindleRandom forest regressionThermal elongationThermal tilt anglesThermal error modeling
英文摘要: Data samples (temperature and thermal drifts) obtained in motorized spindle thermal experiments are usually small and contain much information. Some of the information cannot be fully comprehended by most regression modeling methods when modeling with small training samples; hence, the modeled thermal error predictors can seriously lack robustness, especially for the thermal tilt angle (vital for the boring accuracy of precision boring machines) predictors. To solve this problem, the LS-MLR (least-squares multivariable linear regression), the GA-SVR (genetic algorithm-support vector machine for regression), and the RFR (random forest regression) regression modeling methods are applied to construct the thermal error predictors (models) with the training sample, and the predictors are then...
原始语种摘要: Data samples (temperature and thermal drifts) obtained in motorized spindle thermal experiments are usually small and contain much information. Some of the information cannot be fully comprehended by most regression modeling methods when modeling with small training samples; hence, the modeled thermal error predictors can seriously lack robustness, especially for the thermal tilt angle (vital for the boring accuracy of precision boring machines) predictors. To solve this problem, the LS-MLR (least-squares multivariable linear regression), the GA-SVR (genetic algorithm-support vector machine for regression), and the RFR (random forest regression) regression modeling methods are applied to construct the thermal error predictors (models) with the training sample, and the predictors are then...
全文获取路径: Springer Nature  (合作)
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关键词翻译
关键词翻译
  • modeling 制祝型
  • motorized 机动化的
  • thermal 热的
  • error 误差
  • training 培养
  • machine 机器
  • sample 样品
  • boring 钻进
  • dirty 肮脏的
  • spindle 纺锤体