Online estimation of the heat flux during turning using long short-term memory based encoder-decoder

Abstract

Heat flux during machining has received extensive attention due to its importance for understanding the cutting mechanism and promising prospects on intelligent manufacturing. Research on heat flux estimation by inverse heat conduction methods faces many challenges, including measurement error amplification, stability of the methods, and limitations for applications. In this paper, we introduce a long short-term memory (LSTM) based encoder-decoder (ED) scheme in online estimation of the heat flux imposed at the tool-chip region during turning. The math-physical model and finite element model are established to generate training datasets. Numerical tests using simulated heat flux and temperature data representing different machining conditions are carried out to evaluate the method performance. Compared with other artificial intelligence methods such as multilayer perceptron, convolutional neural networks and LSTM, the LSTM-ED model performs better at all tested noise levels (1≤σ≤20K) with acceptable time cost for online process. Effects of the location and number of sensors on the accuracy of heat flux estimations are also investigated. Experimental validations based on cutting temperature measurements by five thermocouples located in the insert are conducted. Both numerical and experimental tests indicate the potential of the LSTM-ED method for online heat flux monitoring in scientific research and industrial production.