Research on the Recognition of Machining Conditions Based on Sound and Vibration Signals of a CNC Milling Machine

Machining conditions of real-time identification tools is a key and trending issue for the industry. This paper focuses on identifying whether machining is performed as well as the chatter conditions generated during re-machining processes. Identifying whether or not machining conditions are met allows users to ensure the normal operation of machining equipment and identify situations that do not match the current conditional, so that they can take early action and further save on operational costs for machining. The objective of this paper is to identify the milling machining conditions, and the identified conditions will be categorized into whether cutting is required as well as whether chatter is observed. In order to identify these three conditions, sound and vibration signals are captured by sensors inside the milling machine, and the process of identification is subsequently analyzed and conditions established. In this paper, in order to produce a valid model, the extracted machining signal is characterized as a training model by the properties of Approximate Entropy and Short-Time Fourier Transform, and the k-fold cross-validation criteria is utilized to present the identification results. Finally, In this study, the model recognition rate of support vector machine with approximate entropy was 91.4%. The recognition rate of the convolutional neural network with short time span Fourier transform was 95.5%. Finally, the reduced network architecture can significantly reduce the training time and maintain the recognition rate at 93.6%.

Automated summary

This study focuses on real-time identification of machining conditions and chatter conditions in the machining process. Sound and vibration signals are captured and analyzed to identify whether machining is performed and whether chatter is observed. Experimental results show that support vector machine and convolutional neural network can effectively identify machining conditions, and a reduced network architecture can reduce training time while maintaining a high recognition rate.