数模联动下电主轴智能热误差测量与预测  

Model-data fusion based thermal error measurement and prognostics for motorized spindles

作　　者：丁鹏 丁爽 赵孝礼 张虎 李传江 DING Peng;DING Shuang;ZHAO Xiaoli;ZHANG Hu;LI Chuanjiang(College of Mechanical Engineering,Yangzhou University,Yangzhou 225127,China;School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China;Gongda CNC Technology Co.,Ltd.,Nanjing 211800,China;School of Mechanical Engineering,Guizhou University,Guiyang 550025,China)

机构地区：[1]扬州大学机械工程学院,扬州江苏225127 [2]南京理工大学机械工程学院,南京江苏210094 [3]南京工大数控科技有限公司,南京江苏211800 [4]贵州大学机械工程学院,贵州贵阳550025

出　　处：《中国测试》2024年第9期56-64,共9页China Measurement & Test

基　　金：国家自然科学基金(52305589);江苏省自然科学基金(BK20220950);扬州市“绿扬金凤”计划(YZLYJFJH2023YXBS126)。

摘　　要：目前机床主轴热误差预测研究未能有效关联物理退化规律与机床状态数据,导致传感测点布局冗余、预测模型解释性和准确性不足等困境。基于数模联动思想结合有限元数值建模与人工智能算法对成型磨齿机电主轴砂轮端几何误差进行了精确预测。首先建立磨齿机主轴有限元数值模型确定稳态温度场测点可行域,随后基于多目标优化算法开发了一种兼具无监督与有监督属性的温度测点精简布局方法;进一步地借助时序预测中自回归建模理论,提出了多通道逆Transformer算法并依托编码-解码架构将温升信号与热误差形变建立变步长映射关系,改善了长迟滞步长所导致的热误差预测泛化弱的难题。最终通过成型磨齿机磨削实验验证了数模联动下智能热误差预测方法的有效性。Nowadays,thermal error prediction of machine tool spindles has not effectively linked physical degradation patterns with machine tool state data,resulting in redundant sensor layouts and insufficient interpretability and accuracy in predictive models.To tackle these challenges,this study accurately predicted the geometric error at the grinding wheel end of a forming grinding machine's motorized spindle by employing the approach of numerical modeling and data-driven decision-making fusion.First,a finite element numerical model of the grinding machine spindle was created to identify feasible regions for steady-state temperature field measurement points.Following this,a temperature measurement point layout method that combines unsupervised and supervised learning was developed using a multi-objective optimization algorithm.In addition,an autoregressive modeling theory in time-series prediction was utilized to design the multi-channel inverted Transformer algorithm.This algorithm,leveraging an encoder-decoder architecture,maps temperature signals to thermal error deformation in variable-length prediction mode,overcoming weak generalization in thermal error prediction caused by long lag steps.Ultimately,the effectiveness of this intelligent thermal error prediction method within the digital-physical linkage framework was validated through grinding experiments on the grinding machine.

关 键 词：电主轴 热误差预测 数模联动 多目标优化 智能算法 

分 类 号：TB9[一般工业技术—计量学] TP391[机械工程—测试计量技术及仪器]