文章摘要
李高强,张宇,李鸣.基于GA-LSSVM的数控机床热误差建模方法研究[J].机床与液压,2021,49(2):26-30.
LI Gaoqiang,ZHANG Yu,LI Ming.Study on Thermal Error Modeling Method for CNC Machine Tool Based on GA-LSSVM[J].Machine Tool & Hydraulics,2021,49(2):26-30
基于GA-LSSVM的数控机床热误差建模方法研究
Study on Thermal Error Modeling Method for CNC Machine Tool Based on GA-LSSVM
  
DOI:10.3969/j.issn.1001-3881.2021.02.006
中文关键词: 最小二乘支持向量机  热误差  遗传算法  数控机床
英文关键词: Least squares support vector machine(LSSVM)  Thermal error  Genetic algorithm(GA)  CNC machine tool
基金项目:江西省重点研发计划项目(20161BBE50084);河北省重点研发计划项目(16211803D)
作者单位E-mail
李高强 南昌大学信息工程学院 452225311@qq.com 
张宇 南昌大学信息工程学院  
李鸣 南昌大学信息工程学院  
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中文摘要:
      为减小数控机床热误差对加工精度的影响,实现对热误差的补偿控制,提出一种基于遗传算法(GA)优化的最小二乘支持向量机(LSSVM)数控机床热误差建模方法。利用遗传算法优化选择LSSVM惩罚因子C和核函数参数σ2,构建针对某卧式加工中心主轴热误差的GA-LSSVM模型。根据该模型得到热误差的模拟值和测量值对比曲线,通过分析发现GA-LSSVM模型性能较好,模型残差较小,预测精度较高。建立热误差LSSVM模型和传统BP模型并与GA-LSSVM模型作对比,结果表明:GA-LSSVM模型绝对残差δ及均方误差MSE均为最小,模型决定系数R2最大,验证了GA-LSSVM建模方法的有效性
英文摘要:
      In order to reduce the influence of thermal error of CNC machine tool on machining accuracy and to realize the compensation control for thermal error, a thermal error modeling method for CNC machine tool based on least squares support vector machine(LSSVM) optimized by genetic algorithm(GA) was proposed. The genetic algorithm was used to optimize the selection of LSSVM penalty factorCand the kernel function parameter σ2, a thermal error GA-LSSVM model for a horizontal machining center spindle was constructed. According to this model, the comparison curve of the simulated value and measured value of the thermal error was obtained. Through analysis, it was found that the GA-LSSVM model had good performance, small residual and high prediction accuracy. The thermal error LSSVM model and traditional BP model were established and they were compared with the GA-LSSVM model. The results show that the absolute residualδand mean square error (MSE) of GA-LSSVM model are both the minimum, and the model determination coefficient R2 is the maximum. It proves the effectiveness of the GA-LSSVM modeling method
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