Online roundness prediction of grinding workpiece based on vibration signals and support vector machine

This paper proposes an online prediction method for the roundness of grinding workpieces based on vibration signals. Vibration sensors are used to collect vibration signals during grinding, and wavelet packet denoising is used to preprocess original signals to obtain effective vibration signals. Then use time domain analysis and frequency domain analysis to extract features and normalize them to form feature vectors. The roundness of the finished workpiece is measured using a shape-measuring instrument and integrated with the feature vectors to generate a usable data set. The support vector machine (SVM) algorithm is implemented using A Library for Support Vector Machines (LIBSVM), and a prediction model is constructed. Use the data set to train the model and evaluate the accuracy of the model to verify the effectiveness of the model. The results show that the prediction accuracy of the prediction method can reach 92.86%, and it can better predict whether the roundness is qualified.