A Study on Machine Learning Application by Convolutional Neural Network Model Classifying Audio to Identify Vibration Phenomenon in the Turning Process

This study proposes a method for detecting chatter during machining by classifying the recorded sound produced during machining. The sound is recorded during machining with different cutting speeds, and abnormal vibration is determined based on the quality of the machined surface (stable or vibrating). After machining, the sound is processed and classified using the deep learning model VGG16. The sound data is represented as a spectrogram image, which is used to train the image classification model. The results showed that the model achieved an accuracy of 94%. Furthermore, the results demonstrate that sound data is sufficient to identify chatter during machining, and sound classification can be used to develop remote monitoring tools to improve productivity and quality of mechanical machining products.