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

作　　者：Weixin Xu Huihui Miao Zhibin Zhao Jinxin Liu Chuang Sun Ruqiang Yan 

机构地区：[1]School of Mechanical Engineering,Xi’an Jiaotong University,Xi’an 710049,China [2]State Key Laboratory for Manufacturing Systems Engineering,Xi’an Jiaotong University,Xi’an 710049,China

出　　处：《Chinese Journal of Mechanical Engineering》2021年第3期130-145,共16页中国机械工程学报（英文版）

基　　金：Supported in part by Natural Science Foundation of China(Grant Nos.51835009,51705398);Shaanxi Province 2020 Natural Science Basic Research Plan(Grant No.2020JQ-042);Aeronautical Science Foundation(Grant No.2019ZB070001).

摘　　要：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.

关 键 词：Tool wear prediction MULTI-SCALE Convolutional neural networks Gated recurrent unit 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TG71[自动化与计算机技术—控制科学与工程]