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基于线性响应面法的机床几何误差灵敏度识别 被引量:2

A Sensitivity Identification for Geometric Error of Machine Tool Based on Linear Response Surface Methodology
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摘要 机床各零部件几何误差是影响机床精度的主要因素,识别各项几何误差对机床精度的灵敏度可以为机床精度分析提供理论依据与参考。基于多体系统和线性响应面法,提出了一种对机床精度解析模型的灵敏度识别方法。以一台5轴卧式数控铣床为例,对提出的模型和算法进行了应用,首先建立机床的几何精度模型,利用插值抽样技术提取模型样本点,从而构造机床的几何精度线性响应面模型,通过计算与分析该模型的敏感度系数,从而识别出机床的关键性几何误差。计算示例表明,该方法可以有效计算出机床各项几何误差的敏感度系数,对于机床几何误差参数较多且各项误差耦合关系复杂时有一定的适用性。 The geometric errors of parts is the main reason affecting the machining accuracy.Identify the sensitivity coefficient of machining geometric error can provide important theoretical basis for the analysis of machining accuracy.Based on the theory of multi-body system and linear response surface methodology,a new method of identifying the sensitivity of the machining geometric accuracy is proposed.Taking a five-axis milling machine tool as example,apply the machining geometric accuracy model and algorithm.Firstly,build an error model of target machine tool,then use the technique of sampling to extract sample points so that the linear response surface model of geometric accuracy for the machine tool can be established,finally the key geometric error are identified after sensitivity coefficient of error are calculated and analyzed.Calculation show that the sensitivity coefficient of machine geometric accuracy can be identified effectively and has some applicability for the geometric accuracy model with more parameters and the complex coupling among them.
作者 肖龙帆 杜群贵 吴磊 翟晓晨 XIAO Long-fan;Du Qun-gui;WU Lei;ZHAI Xiao-chen(School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640,China;The State Key Laboratory of Mechanical Transmission,Chongqing University,Chongqing 400044,China)
出处 《组合机床与自动化加工技术》 北大核心 2018年第4期97-101,共5页 Modular Machine Tool & Automatic Manufacturing Technique
基金 广东省自然科学基金项目(2016A030313514) 广东省科技发展专项(2017B090910010) 重庆大学机械传动国家重点实验室2015年度开放基金(SKLMT-KFKT-201503)
关键词 多体系统理论 几何精度 线性响应面法 灵敏度分析 multi-body system theory geometric accuracy linear response surface methodology sensitivity
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