中国机械工程
中國機械工程
중국궤계공정
CHINA MECHANICAl ENGINEERING
2015年
2期
217-222
,共6页
刀具磨损量预测%最小二乘支持向量机%经验模态分解%自回归模型
刀具磨損量預測%最小二乘支持嚮量機%經驗模態分解%自迴歸模型
도구마손량예측%최소이승지지향량궤%경험모태분해%자회귀모형
tool wear prediction%lease square support vector machine(LS-SVM)%empirical mode decomposition(EMD)%auto regressive(AR)model
提出了基于最小二乘支持向量机回归算法的刀具磨损量预测方法。该方法首先利用经验模态分解算法对非线性、非平稳的声发射信号进行平稳化处理，得到了若干个固有模态函数；然后建立了每个固有模态函数的自回归模型，并提取模型系数构造特征向量；最后采用最小二乘支持向量机回归算法实现了刀具磨损量的预测。该方法与神经网络预测算法相比，具有更高的预测准确率，可有效预测当前切削状态下10 s后的刀具磨损量。
提齣瞭基于最小二乘支持嚮量機迴歸算法的刀具磨損量預測方法。該方法首先利用經驗模態分解算法對非線性、非平穩的聲髮射信號進行平穩化處理，得到瞭若榦箇固有模態函數；然後建立瞭每箇固有模態函數的自迴歸模型，併提取模型繫數構造特徵嚮量；最後採用最小二乘支持嚮量機迴歸算法實現瞭刀具磨損量的預測。該方法與神經網絡預測算法相比，具有更高的預測準確率，可有效預測噹前切削狀態下10 s後的刀具磨損量。
제출료기우최소이승지지향량궤회귀산법적도구마손량예측방법。해방법수선이용경험모태분해산법대비선성、비평은적성발사신호진행평은화처리，득도료약간개고유모태함수；연후건립료매개고유모태함수적자회귀모형，병제취모형계수구조특정향량；최후채용최소이승지지향량궤회귀산법실현료도구마손량적예측。해방법여신경망락예측산법상비，구유경고적예측준학솔，가유효예측당전절삭상태하10 s후적도구마손량。
Aiming at online predicting tool wear accurately,a method based on the regression algo-rithm of LS-SVM was proposed.First the acoustic emission signals were decomposed into several in-trinsic mode functions(IMF)employing empirical mode decomposition.Then,an AR model of each IMF was established respectively.AR model coefficients were extracted to construct feature vector.Fi-nally,the feature vectors were feed into LS-SVM and prediction of tool wear was realized.The experi-mental results show that it can predict the amount of tool wear after 10s according to the current cut-ting conditions and the proposed method has better accuracy compared with neural network algo-rithm.
