Gated Recurrent Units Based Neural Network For Tool Condition Monitoring

Abstract

Tool condition monitoring (TCM) is a prerequisite to ensure high finishing quality of workpiece in manufacturing automation. One of the most important components in TCM system is tool wear estimation. How to achieve estimation with high accuracy is still an open question. In the past few decades, recurrent neural network (RNN) has shown a great success in learning long-term dependence of the sequential data. However, traditional RNNs (e.g., vanilla RNN, etc.) suffer gradient vanishing or exploding problem as well as long computational training time when the model is trained through back propagation through time (BPTT). To address these issues, we propose a gated recurrent units (GRU) based neural network to estimate the tool wear for tool condition monitoring. The GRU neural network can analyze time-series data on multiple time scales and can avoid gradient vanishing during training. A real-world gun drilling experimental dataset is used as a case study for tool condition monitoring in this paper. The performance of the proposed GRU based TCM approach is compared with other well-known models including support vector regression (SVR) and multi-layer perceptron (MLP). The experimental results show that the proposed GRU based TCM approach outperforms other competing models on this real-world gun drilling dataset.