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Comparative analysis on recent deep learning techniques for identifying chatter in milling process

Milling seems to be the most extensively utilized production technology in modern manufacturing industries, and it plays a significant role. Chatter is a type of disturbance in the form of vibration that has a negative impact on machining operation. Chatter recognition utilizing sensor outputs is a hot topic in academia. Although some progress has indeed been documented utilizing various featurization techniques and ml techniques, conventional approaches have a number of limitations, including manual preparation and a huge dataset need. Although, these are widely being used to evaluate milling operations in terms of production efficiency & work piece surface quality,.they are not suited for real applications due to their computing duration and require large data for training process. Therefore, in this study, three well-performing deep learning approaches such as LSTM, DWT, and Bi-LSTM are used to provide an effective way for monitoring and managing chatter in the milling processes with the Duplex 2205 material. Here, some of the parameters like acceleration is measured while the milling operation is taking place, and the measured acceleration value is processed using selected three DL techniques for identifying the presence of chatter and are tested to see which one performs the best. The Bi-LSTM outperformed other approaches in detecting chatter present, according to the data.