CAJ | 학술논문

为了提高金属铣削过程中的刀具磨损状态识别的自动化程度与精度，提出了基于局部切空间排列（LTSA）方法与隐 Markov 模型（HMM）来识别刀具的不同磨损状态的方法。该方法首先利用小波分析技术对铣削过程中的切削进给方向力信号进行处理，构造了高维特征空间。然后使用基于流形学习方法实现了高维特征空间的维数约简。最终利用约简后的低维特征向量训练 HMM，从而实现刀具磨损状态的识别。实验结果说明该方法能够有效地识别铣削过程的刀具磨损状态。与未经特征维数约简的识别方法相比，新方法能够提高刀具磨损状态的识别效率与准确率。
위료제고금속선삭과정중적도구마손상태식별적자동화정도여정도，제출료기우국부절공간배렬（LTSA）방법여은 Markov 모형（HMM）래식별도구적불동마손상태적방법。해방법수선이용소파분석기술대선삭과정중적절삭진급방향력신호진행처리，구조료고유특정공간。연후사용기우류형학습방법실현료고유특정공간적유수약간。최종이용약간후적저유특정향량훈련 HMM，종이실현도구마손상태적식별。실험결과설명해방법능구유효지식별선삭과정적도구마손상태。여미경특정유수약간적식별방법상비，신방법능구제고도구마손상태적식별효솔여준학솔。
In order to improve the automation and the precision of tool wear condition recognition in the process of metal milling, we proposed the method based on the manifold learning———the local tangent space alignment (LT?SA) method———and the hidden Markov model (HMM) to identify tool wear conditions. First, this method used the time domain and the wavelet analysis technique for signal processing of the milling cutting axial force to construct the high dimensional feature space. Then, the local tangent space alignment (LTSA) method was used to achieve the dimensionality reduction. At last, the low dimensional feature vector was used to train the HMM in order to rec?ognize tool wear conditions. Additional tests were conducted to check the feasibility of the method. Comparison of the performance of the proposed method with that of the method of identification without the feature dimension re?duction shows that the proposed method can improve the efficiency and the accuracy of tool wear condition recogni?tion.