Study on Bandwidth Analyzed Adaptive Boosting Machine Tool Chatter Diagnosis System

This paper presents an Adaboost algorithm based cutting data analysis for chatter detection. This offline chatter analysis uses the vibration data collected by accelerometers attached to the spindle housing. A comparison of the accuracy achieved with support vector machine, Random Forest, 1D Convolutional Neural Networks and Multilayer Perceptron algorithm is also made. In this paper, the accelerometer data are transformed into bandwidth. Time-accelerometer and time-spectral bandwidth learning models are built in order to realize chatter detection and automated machine learning. A comparison of the models is made. The results of cross validation indicate that an accuracy of 98% is achieved, which is made possible by using the bandwidth signals that are transformed from accelerometer data. Experimental results show that applying the Adaboost algorithm to analyze the spectral data transformed from vibration signals and using them to detect chatters has higher reliability and accuracy compared to other algorithms and analyzing other transform signals.

Automated summary

This paper presents an Adaboost algorithm for chatter detection in cutting data analysis. By transforming and analyzing the accelerometer data, learning models are built and compared with different algorithms. The experimental results show that using the transformed bandwidth signals achieves higher accuracy and reliability.