Acoustic emission detection of blunting states of grinding wheel based on VMD-PNN

During the grinding process, the blunting phenomenon occurs on the processing tool, i.e. the grinding wheel. The wear of the grinding wheel surface affects the machining accuracy and the quality of the workpiece, and it needs to be detected and repaired in time. The plastic deformation, fragmentation, and fracture of the abrasive particles will generate acoustic emission (AE) signals, which can be used as a basis identifying the blunting states of the grinding wheel, and it is not easy to be disturbed by noise. Therefore, an AE detection method of grinding wheel blunting states based on variational mode decomposition (VMD) and probabilistic neural network (PNN) is proposed. VMD can decompose the original signal into multiple intrinsic mode function (IMF) components, and filter out the components with larger kurtosis to reconstruct AE signal. The key to AE detection is the selection of characteristic parameters. Based on the related researches, the proportion of envelope energy is presented as an important characteristic parameter, and a total of 5 characteristic parameters are selected to construct a five-dimensional characteristic vector dataset and input to PNN for training. After testing, the recognition accuracy reaches 94.5%. This method establishes the relationship between the characteristic parameters of AE signal and the different blunting states of grinding wheel, which can accurately predict the severe blunting state of grinding wheel and has practical application value. Moreover, the accuracies using different characteristic parameters of AE signals to identify the blunting states of grinding wheel are compared in this paper, which has reference significance for the selection of characteristic parameters.