Intelligent Chatter Detection in Milling Using Vibration Data Features and Deep Multi-Layer Perceptron

Abstract

Milling is a highly crucial machining process in the modern industry. With the recent trends of Industry 4.0, it is becoming more common to implement Artificial Intelligence (AI) methods to increase the performance of milling processes. As a significant limitation for the efficiency of the machining processes, chatter detection, and avoidance are critical. In this paper, a chatter detection method based on vibration data features for the slot milling process is proposed. This method benefits from a deep learning method, Deep Multi-Layer Perceptron (DMLP). Vibration data was acquired by attaching an accelerometer to the spindle housing during slot milling operations. Fast Fouries Transform (FFT) was applied to time-domain vibratory data. Frequency domain data achieved by FFT was investigated for labeling the occurrence of chatter. These labels were used to train the DMLP algorithm. Time-domain signal features such as root mean square, clearance factor, skewness, crest factor, and shape factor were selected as inputs for the chatter detection algorithm. Finally, validation cuttings were performed for verifying the results of the DMLP algorithm. The results prove that time-domain features can provide enough information about the chatter occurrence in slot milling operations, and the DMLP algorithm proposed in this research can successfully detect the chatter occurrence.