摘要
针对数控机床热误差补偿技术中温度布点选取的问题,提出了基于前馈神经网络的自适应矢量量化(AVQ)网络聚类的方法,将AVQ网络聚类法应用于一台加工中心并将18个测点减少到3个,基于输出-输入反馈Elman(OIF-Elman)神经网络模型建立了机床热误差与关键测点温度之间的关系.结果表明,采用基于AVQ网络聚类法和OIF-Elman神经网络预测模型,能够降低机床温度测点之间耦合作用的影响,提高热误差建模的准确性与鲁棒性.
An adaptive vector quantization （AVQ） network clustering algorithm based on feed forward neu- ral network for the selection of temperature measuring points was proposed in thermal error compensation on machine tools. The method reduces the measuring points from 18 to 3, when adopted on a machining center. Then the relationship between thermal error and key temperature measuring points was established based on the output input feedback Elman neural network model. The experimental results show that the method proposed can effectively eliminate the coupling among temperature measuring points and improve the accuracy and robustness of the thermal error model.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2014年第1期16-21,共6页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金项目(51275305)
国家科技重大专项(2011ZX04015-031)
高等学校博士学科点专项科研基金项目(20110073110041)资助
关键词
数控机床
自适应矢量量化网络
输出一输入反馈Elman神经网络
热误差建模
computer numerical control （CNC） machine tool
adaptive vector quantization （AVQ）network
output input feedback Elman neural network
thermal error modeling
