Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

Weixin Xu,Chuang Sun,Huihui Miao,Zhibin Zhao,Ruqiang Yan,Jinxin Liu

doi:10.1186/s10033-021-00565-4

Weixin Xu, Chuang Sun + Show 4 more

Open Access

https://doi.org/10.1186/s10033-021-00565-4

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Abstract

As an integrated application of modern information technologies and artificial intelligence, Prognostic and Health Management (PHM) is important for machine health monitoring. Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry. In this paper, a multi-scale Convolutional Gated Recurrent Unit network (MCGRU) is proposed to address raw sensory data for tool wear prediction. At the bottom of MCGRU, six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network, which augments the adaptability to features of different time scales. These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations. At the top of the MCGRU, a fully connected layer and a regression layer are built for cutting tool wear prediction. Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

Highlights

The development of Prognostic and Health Management (PHM) has motivated the research in the field of machine health monitoring to detect faults and predict machine’s future conditions [1,2,3,4]
We propose a model combining multiscale Convolutional Neural Networks (CNN) and GRU named Multi-scale Convolutional Gated Recurrent Unit Network (MCGRU) to predict cutting tool wear
4.3 Results the comparison based on the Mean Absolute Error (MAE) and RMSE of the above models are shown

Summary

Introduction

The development of Prognostic and Health Management (PHM) has motivated the research in the field of machine health monitoring to detect faults and predict machine’s future conditions [1,2,3,4]. It is crucial to monitor and predict the cutting tool wear online so as to prevent the quality from degradation [6]. Aiming at monitoring the working conditions of cutting tools and predicting tool wear, many methods, direct or indirect, online or offline, have been researched. By performing cutting tests under different working conditions, data about the cutting tools are acquired and analyzed with the help of optimization techniques including the response surface methodology (RSM) and the design of experiments (DOE). This approach is time-consuming and inefficient because the number of the tests required is large [7]. Özel et al [14] used neural networks for the prediction of cutting tool wear and its surface roughness

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Results

Conclusion