Abstract
Tool wear condition monitoring (TCM) is essential for milling process to ensure the machining quality, and the long short-term memory network (LSTM) is a good choice for predicting tool wear value. However, the robustness of LSTM- based method is poor when cutting condition changes. A novel method based on data fusion enhanced LSTM is proposed to estimate tool wear value under different cutting conditions. Firstly, vibration time series signal collected from milling process are transformed to feature space through empirical mode decomposition, variational mode decomposition and fourier synchro squeezed transform. And then few feature series are selected by neighborhood component analysis to reduce dimension of the signal features. Finally, these selected feature series are input to train the bidirectional LSTM network and estimate tool wear value. Applications of the proposed method to milling TCM experiments demonstrate it outperforms significantly SVR- based and RNN- based methods under different cutting conditions.
Highlights
In the modern numerical control milling process, tool condition is one of the key factors affecting the machining quality of workpiece [19, 22]
Vibration time series signal collected from milling process are transformed to feature space through Empirical mode decomposition (EMD), Variational mode decomposition (VMD) and Fourier synchro squeezed transform (FSST), and few feature series are selected by neighborhood component analysis (NCA) to reduce dimension of the signal features
The long short-term memory network (LSTM) model established by the training set and verification set is applied to predict the testing set, including 4 tools with different cutting conditions
Summary
In the modern numerical control milling process, tool condition is one of the key factors affecting the machining quality of workpiece [19, 22]. Tool breakage is the main cause of abnormal shutdown and lead to time lost and capital destroyed [27]. Traditional tool condition monitoring (TCM) methods are based on the machining time or the number of workpiece machined resulting in the effective utilization rate of tool is only 50%-80%, which affect the processing efficiency and increase the machining cost significantly [15, 35]. It is predicted that an effective TCM method can increase the cutting efficiency by 10-50% and reduce the machining cost by 10-40% [23, 33]. The development of effective online TCM method has received broadly positive reviews and is a research hotspot nowadays [10, 11]
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