基于声信号与SVM的机械加工表面粗糙度在线监测OA

To address the need for online monitoring of surface roughness during hard turning,acoustic signals combined with a support vector machine(SVM)are employed for machining quality diagnosis,and principal component analysis(PCA)is applied to reduce the dimensionality of the acoustic features.The results show that the extracted first principal component is strongly correlated with material removal rate and surface roughness parameters,while the third principal component is closely related to cutting speed.Based on this correlation,an SVM classifier using the RBF kernel function is developed to identify three different levels of surface roughness.Under the optimal configuration,the method achieves a recognition accuracy of 100.0%.The study confirms that acoustic signals can serve as an effective tool for monitoring surface roughness during hard turning,providing a new solution for online quality monitoring in machining processes.