基于EAKI辨识策略的机床振动试验研究  

Experimental Study on Machine Tool Variation Based on EAKI Method

作　　者：黄子凌 刘成颖[1,2] 李铁民[1,2] 

机构地区：[1]清华大学机械工程系,北京100084 [2]精密超精密制造装备及控制北京市重点实验室,北京100084

出　　处：《组合机床与自动化加工技术》2016年第2期43-46,50,共5页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：国家04科技重大专项课题(2013ZX04001-021;2014ZX04001051)

摘　　要：机床振动是制约机床加工精度及效率的核心因素,文章基于EAKI策略在多台机床上开展振动试验研究。针对强迫振动及自激振动信号辨识问题,首先提出EAKI振动信号辨识策略;为有效构建振动信号备案知识库,提出信号特征分量提取算法;最后在国内外多台数控机床上进行了全转速状态下的振动试验研究。试验结果有效地验证了EAKI策略的可行性与实用性,同时为机床性能评估及设计完善提供了良好借鉴。Vibration is the key factor that constrains high quality and efficiency of a machine tool. The paper studied a series of experiments on different machine tools based on EAKI method. In order to achieve signal identification between chatter and forced vibration, we proposed EAKI method which combined theoretical analysis, experimental study and database technology. Then we proposed a feature extraction method which can be applied to record useful vibration information in order to construct vibration feature database. At last we conducted a series of vibration experiments on machine tools made in China and abroad. The experimental results verified the feasibility of EAKI strategy effectively and practically, at the same time it provided a good reference for the performance evaluation and redesign of machine tool.

关 键 词：EAKI方法 信号辨识 振动试验 特征提取 

分 类 号：TH161.6[机械工程—机械制造及自动化] TG506[金属学及工艺—金属切削加工及机床]