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首页> 《中国测试》期刊 >本期导读>基于参数识别的贴片机键合臂约束工况模态提取与特征分析

基于参数识别的贴片机键合臂约束工况模态提取与特征分析

2788    2019-04-28

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作者:宫文峰1,2, 张美玲2, 陈辉1, 王鑫2

作者单位:1. 武汉理工大学 能源与动力工程学院, 湖北 武汉 430063;
2. 桂林电子科技大学(北海校区) 海洋信息工程学院, 广西 北海 536000


关键词:模态分析;参数识别;特征提取;贴片机;振动测试


摘要:

为掌握键合臂的模态特性对保障贴片机键合精度的影响,建立键合臂的模态特性综合对比分析模型,以简化安装工况下的键合臂为对象,通过理论计算与实验测试相结合的方法对比分析键合臂的约束模态特性。通过ANSYS有限元数值计算得到前6阶振型特征,并以此为参考确定实验中激振点布置方案,然后联合使用LMS Test.Lab锤击系统和POLYTEC激光测振仪等工具对键合臂进行模态实验。通过模态参数特征识别和提取,经一致性对比分析,得到键合臂准确的模态参数和刚度分布情况。研究表明,键合臂的一阶固有频率为306 Hz,远高于外部激振源最高频率,动态特性满足设计要求。研究结果为贴片机键合臂的结构轻量化、结构优化及整机的防振、抑振等提供理论支撑和实验参考。


Modality extraction and feature analysis of chip bonder bonding arm under the constraint working conditions based on parameter identification
GONG Wenfeng1,2, ZHANG Meiling2, CHEN Hui1, WANG Xin2
1. School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China;
2. School of Marine Information Engineering(Beihai Campus), Guilin University of Electronic and Technology, Beihai 536000, China
Abstract: In order to grasp the influence of the modal characteristics of the picking-up arm on the bonding precision of the chip bonder. The comprehensive comparison and analysis model of the modal characteristics of the picking-up arm is established. The constraint modality characteristic of bonding arm under the simplified installation condition is analyzed by finite element calculation and modal test. Theoretical and experimental results were compared and discussed. Exact modal characteristics, weak link and its influential factors of bonding arm are obtained. The results show that the first order inherent frequency is 306 Hz, is higher than external excitation frequency. This research result provides a theoretical support and experiment reference for optimizing structure, preventing resonance and reduces vibration of bonding arm.
Keywords: modality analysis;parameter identification;characteristic extraction;chip bonder;vibration test
2019, 45(4):29-34  收稿日期: 2017-12-08;收到修改稿日期: 2018-02-28
基金项目: 国家自然科学基金(51579200);广西高校中青年教师科研基础能力提升项目(2019KY0216)
作者简介: 宫文峰(1987-),男,山东泰安市人,讲师,博士,研究方向为船舶机械振动与故障诊断技术、模式识别及深度学习
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