摘要
为提高数控机床热误差预测的准确性和适应性,提出一种基于序列深度学习网络的数控机床热误差建模和预测方法。提出一种基于LSTM的序列深度学习预测网络,构建包含历史序列数据的动态数据矩阵为模型输入;通过截断式训练方法降低深度预测网络中每项参数更新的复杂度,利用序列深度学习网络自动提取温度时间序列的时空特征,准确表征温度序列信号与热误差之间复杂的映射关系,采用Softmax输出层对热误差进行准确预测。实验结果表明:该方法解决了传统浅层方法因未考虑历史序列数据对当前输出的影响而存在的预测精度不高、鲁棒性差等问题,将热误差预测的均方根误差降低到2.5μm以内,优于传统的SVM和BP等浅层神经网络预测方法,为数控机床热误差补偿提供了参考。
In order to improve the accuracy and adaptability of thermal error prediction for NC machine tools,a thermal error modeling and prediction method for NC machine tools based on sequence deep learning network was proposed.A sequence deep learn⁃ing prediction network based on LSTM was presented.The dynamic data matrix containing historical sequence data was established as input of the model.The truncated training method was used to reduce the complexity of each parameter update in the deep prediction network.The timing feature of the temperature time sequence was automatically extracted by using the sequence deep learning network,and the complex mapping relationship between temperature sequence signal and thermal error was accurately characterized.Thermal er⁃ror was accurately predicted through the Softmax output layer.The experimental results show that by this method,the problems of low accuracy and poor robustness of the traditional shallow method because it does not consider the influence of historical sequence data on the current output are solved,and the root mean square error of thermal error prediction is reduced to less than 2.5μm.The proposed method is better than the traditional SVM and BP prediction methods.It provides reference for thermal error compensation of NC ma⁃chine tools.
作者
杜柳青
余永维
DU Liuqing;YU Yongwei(College of Mechanical Engineering,Chongqing University of Technology,Chongqing 400054,China)
出处
《机床与液压》
北大核心
2020年第23期88-92,共5页
Machine Tool & Hydraulics
基金
国家自然科学基金面上项目(51775074)
重庆市重点产业共性关键技术创新重点研发项目(cstc2017zdcy-zdy⁃fX0066,cstc2017zdcy-zdyfX0073)
重庆市基础研究与前沿探索项目(cstc2018jcyjAX0352)。
关键词
数控机床
热误差
预测
序列深度学习
神经网络
NC machine tool
Thermal error
Prediction
Sequence deep learning
Neural network
