On-line chatter detection in milling with hybrid machine learning and physics-based model

Abstract Unstable vibrations, chatter, in machining lead to poor surface finish and damage to the tool and machine. It is desired to detect and avoid chatter on-line without false alarms for improved productivity. This paper presents the application of a combined machine learning network and physics-based model to detect chatter in milling. The vibration data collected during machining is converted into moving short-time frequency spectrums, whose features are mapped to five machining states as air cut, entry into and exit from the workpiece, stable cut, and chatter conditions. The machine learning network was trained and its architecture was reduced to a computationally optimal network with 3 convolution blocks followed by a neural network with one hidden layer. A parallel algorithm, which Kalman filters the stable forced vibrations to isolate chatter signals in raw data, is used to detect the chatter and its frequency. The combination of the machine learning and physics-based model led to a 98.90% success rate in chatter detection while allowing to further train the network during production with the help of the physics-based, deterministic model.