CAJ | 학술논문

对介观尺度下三向正交的高速微铣削力混合信号提出一种有效的分离算法，从而获得高速微铣削力的真实信号及机床振动信号并基于此对机床状态进行分析。算法首先以信息量为处理标准将观测信号初步分离，再将分离得到的各分量信号作为行向量构造矩阵，最后对该矩阵进行以高斯性最大为度量的分离，逐一得到各激励源信号，并对其快速傅里叶变换得到频谱，结合机床结构及介观尺度下高速微铣削加工特点，识别微铣削力信号及机床状态信息。实验结果分析表明：该方法可成功对介观尺度下高速微铣削加工中主要激励源和机床状态进行有效识别。
대개관척도하삼향정교적고속미선삭력혼합신호제출일충유효적분리산법，종이획득고속미선삭력적진실신호급궤상진동신호병기우차대궤상상태진행분석。산법수선이신식량위처리표준장관측신호초보분리，재장분리득도적각분량신호작위행향량구조구진，최후대해구진진행이고사성최대위도량적분리，축일득도각격려원신호，병대기쾌속부리협변환득도빈보，결합궤상결구급개관척도하고속미선삭가공특점，식별미선삭력신호급궤상상태신식。실험결과분석표명：해방법가성공대개관척도하고속미선삭가공중주요격려원화궤상상태진행유효식별。
An algorithm was proposed for effectively seperating the mixed-signal of three directional high speed micro milling force in mesoscale.The real high speed micro milling force signals and machine vibration signals were then obtained.Based on the algorithm,machine conditions were analyzed.Taking information content as the processing standard,the mixed-signals were separated preliminarily to get the component signals.As row vectors,the component signals obtained were then used to build a matrix.The matrix was further separated according to the rule of most Gaussian.The exciting source signals were sequentially separated out one by one.The results were processed by FFT and transormed to spectrums.Considering the features of high speed micro milling machining in mesoscale,micro milling force signals and conditions of machine were identified.The experiment results show that the algorithm can successfully indentify the main exciting sources and machine conditions during mesoscale high speed micro milling.