摘要
为解决某加工中心电主轴的热误差补偿问题,建立预测精度高、鲁棒性强的热误差补偿模型。搭建实验台,利用美国雄狮回转误差分析仪采集电主轴的温度场和热误差数据。介绍麻雀搜索算法(SSA)原理、具体优化流程。采用SSA优化BP神经网络的权值和阈值,建立SSA-BP神经网络预测模型。与之前建立的BP神经网络预测模型相比,优化后预测效果更优,为电主轴热误差建模提供新的思路。
In order to solve the thermal error compensation problem of electric spindle in a machining center,a thermal error compensation model with high prediction accuracy and strong robustness was established.The experimental platform was set up,and the temperature field and thermal error data of electric spindle were collected by using American Lion rotary error analyzer.Sparrow search algorithm(SSA)principle and specific optimization process were introduced.Using SSA to optimize the weights and thresholds of the BP neural network,the SSA-BP neural network prediction model was established.Compared with the previously established BP neural network prediction model,the prediction effect after optimization is better,which provides a new idea for modeling the thermal error of the electric spindle.
作者
尹晓珊
钟建琳
问梦飞
王增新
YIN Xiaoshan;ZHONG Jianlin;WEN Mengfei;WANG Zengxin(Mechanical Electrical Engineering School,Beijing Information Science&Technology University,Beijing 100192,China;Beijing CTB Co.,Ltd.,Beijing 101500,China)
出处
《机床与液压》
北大核心
2023年第12期19-23,38,共6页
Machine Tool & Hydraulics
基金
北京市科技计划项目(Z191100002019004)。
关键词
热误差补偿
麻雀搜索算法
BP神经网络
Thermal error compensation
Sparrow search algorithm
Back propagation neural network
