An acoustic imaging recognition based cutting tools wear state prediction method

Abstract

Online monitoring of cutting tools wear is an important component of advanced manufacturing technology, which can greatly improve the processing efficiency and reduce the production cost. In this paper, a cutting tools wear state prediction method based on acoustic imaging recognition is developed. By applying the advantages of the functional generalized inverse beamforming method in the sound field reconstruction, the acoustic signal is used as the carrier to reconstruct the three-dimensional space radiated sound field. And then, slice the reconstructed sound field image and input it into the convolutional neural network model as a sample, to process and classify the image and mines the feature information related to state from the sound field image. By incorporating amplitude and phase information of the sound field, the presented method utilizes spatial domain mapping to accurately identify the noise source and address challenges such as low recognition rate and difficult diagnosis under weak fault conditions. Furthermore, the paper also demonstrates the recognition of sound field states through a fault experiment in sound box simulation, based on these theories. And the recognition of sound field states is achieved through a simulation fault experiment conducted on the sound box, thereby validating the feasibility of the state monitoring method based on pattern recognition of sound and image. Finally, the experimental object is selected as the four-edge carbide milling cutter, and the cutting tools wear state is monitored by integrating sound field reconstruction techniques with convolution feature extraction methods to validate the robustness of the proposed approach.