纳米技术与精密工程
納米技術與精密工程
납미기술여정밀공정
NANOTECHNOLOGY AND PRECISION ENGINEERING
2010年
3期
269-274
,共6页
微细铣削%振动信号%时域参数%频域参数%主成分分析
微細鐉削%振動信號%時域參數%頻域參數%主成分分析
미세선삭%진동신호%시역삼수%빈역삼수%주성분분석
micro milling%vibration signal%time domain parameter%frequency domain parameter%principal composition analysis
铣削振动是描述微细铣削加工状态的重要特征参数.利用微小型车铣加工中心、压电加速度计和多通道电荷放大器建立了微细铣削振动测试系统,分别提取了不同铣削方式和铣削转速条件下微细铣削振动信号的时域特征参数和频域特征参数.针对特征参数数量繁多且变化趋势不一致的特点,引入主成分分析方法,利用主成分分别对时域和频域特征参数进行替换,定量描述出振动信号的能量和差异,以及主频带位置和能量分散程度之间的关系.分析结果表明,通过时域与频域特征参数主成分的综合运用,应用较少的参数即可描述微细铣削振动信号的主要特征,显著降低了原始数据维数;主成分分析结果可用于铣削方式和铣削转速等微细铣削加工参数的优化.
鐉削振動是描述微細鐉削加工狀態的重要特徵參數.利用微小型車鐉加工中心、壓電加速度計和多通道電荷放大器建立瞭微細鐉削振動測試繫統,分彆提取瞭不同鐉削方式和鐉削轉速條件下微細鐉削振動信號的時域特徵參數和頻域特徵參數.針對特徵參數數量繁多且變化趨勢不一緻的特點,引入主成分分析方法,利用主成分分彆對時域和頻域特徵參數進行替換,定量描述齣振動信號的能量和差異,以及主頻帶位置和能量分散程度之間的關繫.分析結果錶明,通過時域與頻域特徵參數主成分的綜閤運用,應用較少的參數即可描述微細鐉削振動信號的主要特徵,顯著降低瞭原始數據維數;主成分分析結果可用于鐉削方式和鐉削轉速等微細鐉削加工參數的優化.
선삭진동시묘술미세선삭가공상태적중요특정삼수.이용미소형차선가공중심、압전가속도계화다통도전하방대기건립료미세선삭진동측시계통,분별제취료불동선삭방식화선삭전속조건하미세선삭진동신호적시역특정삼수화빈역특정삼수.침대특정삼수수량번다차변화추세불일치적특점,인입주성분분석방법,이용주성분분별대시역화빈역특정삼수진행체환,정량묘술출진동신호적능량화차이,이급주빈대위치화능량분산정도지간적관계.분석결과표명,통과시역여빈역특정삼수주성분적종합운용,응용교소적삼수즉가묘술미세선삭진동신호적주요특정,현저강저료원시수거유수;주성분분석결과가용우선삭방식화선삭전속등미세선삭가공삼수적우화.
Vibration is one of the most important parameters in micro milling. Based on the establishment of vibration signal measuring system with miniature turn-milling machine tool, tri-axial piezoelectricity accelerometer and multi-channel charge amplifier, vibration signal of three components as well as their time domain and frequency domain parameters were presented within different milling strategies and spindle speeds of micro milling. Principal composition analysis method was introduced considering that the machining condition parameters are various and their trends are significantly different. Time domain and frequency domain parameters were replaced by principal composition, then the relationship between energy and deviation in time domain, power spectrum frequency band and energy distribution in frequency domain were established quantificationally. The analysis results show that the characteristic of vibration signal in micro milling can be described within very limited parameters, which leads to the decrease of dimension of raw experimental data. As a result, the computational result of principal composition is very useful for the optimum selection of micro milling parameters such as milling strategy and spindle speed.