Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network. (July 2021)

• Record Type: Journal Article
• Authors: Liu, Xianli; Liu, Shaoyang; Li, Xuebing; Zhang, Bowen; Yue, Caixu; Liang, Steven Y.
• Is Part Of: Journal of manufacturing systems. Volume 60(2021)
• Page Start: 608
• Page End: 619
• Publication Date: 2021-07
• Subjects: Tool wear -- Tool wear monitoring -- Deep learning -- Convolutional neural network -- Parallel residual network -- Bidirectional long short-term memory network; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2021.06.006
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: A novel integration model is proposed for tool wear monitoring. The parallel residual network is utilized to realize multi-feature fusion and speed up the network learning process. The stacked bidirectional long short-term memory network is designed to encode temporal information. The smoothing method is used to improve the prediction accuracy. Abstract: Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments.