摘要
为了合理分配机床热刚度并为机床零部件的热刚度优化提供依据,提出一种基于热误差神经网络预测模型的机床重点热刚度辨识方法。该方法针对机床不同零部件的热刚度对整机热刚度的影响具有不完全相同的特征,定义一种机床重点热刚度的概念。根据机床温度和热误差试验数据,利用径向基神经网络建模精度高和泛化能力强的特点,建立一种机床热误差神经网络预测模型。以机床不同零部件达到热平衡后产生的单位温升为热误差预测模型的输入矢量,计算热误差变化值作为机床重点热刚度的辨识依据,在此基础上阐述机床重点热刚度辨识方法的原理和实施步骤。将该方法应用在一台高架桥式龙门加工中心的重点热刚度辨识上,辨识结果与验证试验得到的结果相一致。
To distribute the thermal stiffness of a machine tool reasonably and provide the basis for the optimization of thermal stiffness of machine tool parts,an identification method for the key thermal stiffness of a machine tool based on the thermal error neural network prediction model is proposed.Considering the feature that the influences of the thermal stiffness of different parts on the whole machine tool＇s thermal stiffness are non-identical,this method proposes the concept of key thermal stiffness.According to experimental data of temperature and thermal errors,a thermal errors prediction model is built by using radial basis function neural network for its high modeling accuracy and strong generalization ability.Unit temperature risings produced by different machine tool parts after they attain thermal equilibrium are taken as the input vectors of the established prediction model,variation values of thermal errors are calculated to identify the key thermal stiffness.On this basis,the principle and implementation steps of this identification method is presented.Application of the method on a viaduct gantry machining center for its key thermal stiffness identification shows that the identification results are consistent with the verification test results.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2011年第11期117-124,共8页
Journal of Mechanical Engineering
基金
国家'高档数控机床与基础制造装备'科技重大专项资助项目(2009ZX04002-013)
关键词
重点热刚度
辨识
热误差预测模型
神经网络
龙门加工中心
Key thermal stiffness Identification Thermal error prediction model Neural network Gantry machining center
