Non-invasive milling force monitoring through spindle vibration with LSTM and DNN in CNC machine tools

Abstract

Milling force reflects the complex mechanical interaction between machine tool and workpiece, it is of great significance to acquire milling force accurately to monitor and optimize machining process. In this paper, a convenient method to monitor milling force non-invasively is proposed, which can predict milling force based on spindle vibration in real-time. Long and short-term memory network (LSTM) and deep neural network (DNN) are used to build the mapping between milling force and spindle vibration in data-driven manner. Temporal features of training data are extracted through LSTM, while DNN is used to reinforced key features. Accuracy of the proposed method is demonstrated by experiments. Comparing with existing data-driven prediction methods, prediction accuracy is improved at least by 16.7% using LSTM-DNN. This paper provides an easy implemented and accurate approach to acquire milling force of machine tool and has great value for machining process monitoring.