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机床直线轴温度测点筛选与热误差预测方法

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为揭示数控机床直线进给轴温度场信息与热误差之间的非线性映射关系,提高热误差预测模型效率和精度,提出了基于鹈鹕优化算法(POA)与随机森林(RF)的直线轴热误差预测方法.首先,采用Featts聚类算法将温度测点进行聚类分组,通过Spearman相关性分析计算温度与热误差之间的相关程度及热敏感点;其次,使用POA优化算法对RF模型参数进行寻优,确定RF网络的最佳决策树数量及叶子数等;最后,在三轴铣床加工中心上进行实验验证.结果表明,使用Featts聚类算法与Spearman相关分析方法提高了模型精度,有效避免温度测点间的多重共线性.与传统的BP神经网络及RF网络相比,POA-RF预测网络的均方根误差分别降低了46%和43%.
Method of Temperature Measuring Point Screening and Thermal Error Prediction for Feed Axis of Machine Tool
To reveal the nonlinear mapping relationship between the temperature field information of the feed axis of the CNC machine tool and the thermal error,and to improve the efficiency and accuracy of thermal error prediction model,a thermal error prediction method of linear feed axis based on pelican opti-mization algorithm(POA)and random forest(RF)was proposed.First,the Featts clustering algorithm was used to cluster and group the temperature measurement points,Spearman correlation analysis was used to calculate the correlation degree between temperature and thermal error and heat sensitive points.Second-ly,POA optimization algorithm was used to optimize the parameters of the random forest model to deter-mine the optimal number of decision trees and leaves of the RF network Finally,experiments were carried out on a three-axis milling center.The results show that the Featts clustering algorithm and Spearman corre-lation analysis are used to improve the accuracy of the model and effectively avoid multicollinearity among temperature measurement points.Compared with traditional BP neural network and RF network,the root-mean-square error of POA-RF prediction network is reduced by 46%and 43%respectively.

CNC machine toollinear axis thermal errortemperature measuring point screeningthermal error predictionPOA-RF model

丁强强、郭世杰、苏哲、邹云鹤、唐术锋

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内蒙古工业大学机械工程学院,呼和浩特 010051

内蒙古工业大学内蒙古自治区特殊服役智能机器人重点实验室,呼和浩特 010051

数控机床 直线轴热误差 温度测点筛选 热误差预测 POA-RF模型

国家自然科学基金项目国家自然科学基金项目国家自然科学基金项目内蒙古关键技术攻关项目内蒙古自治区高等学校创新团队发展计划支持项目内蒙古自治区直属高校基本科研业务费项目内蒙古自治区直属高校基本科研业务费项目内蒙古自治区高等学校青年科技英才支持计划项目内蒙古自然科学基金项目内蒙古自然科学基金项目

5206505352365058523650642021GG0255NMGIRT2213ZTY2023005JY20230043NJYT230432023LHMS050182023LHMS05017

2024

10.13462/j.cnki.mmtamt.2024.08.005

组合机床与自动化加工技术
大连组合机床研究所 中国机械工程学会生产工程分会

组合机床与自动化加工技术

CSTPCD北大核心
影响因子：0.671
ISSN：1001-2265
年,卷(期)：2024.(8)