Machine Tool Wear Prediction Technology Based on Multi-Sensor Information Fusion

被引:4
|
作者
Wang, Kang [1 ]
Wang, Aimin [1 ]
Wu, Long [2 ]
Xie, Guangjun [3 ]
机构
[1] Beijing Inst Technol, Digital Mfg Inst, Beijing 100081, Peoples R China
[2] Shandong Jianzhu Univ, Sch Mech & Elect Engn, Jinan 250101, Peoples R China
[3] Nanjing Univ Sci & Technol, Dept Mech Engn, Nanjing 210094, Peoples R China
关键词
tool wear prediction; LSTM network; deep residual network; multi-sensor information fusion; DECOMPOSITION; DIAGNOSIS; NETWORK; CHATTER;
D O I
10.3390/s24082652
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The intelligent monitoring of cutting tools used in the manufacturing industry is steadily becoming more convenient. To accurately predict the state of tools and tool breakages, this study proposes a tool wear prediction technique based on multi-sensor information fusion. First, the vibrational, current, and cutting force signals transmitted during the machining process were collected, and the features were extracted. Next, the Kalman filtering algorithm was used for feature fusion, and a predictive model for tool wear was constructed by combining the ResNet and long short-term memory (LSTM) models (called ResNet-LSTM). Experimental data for thin-walled parts obtained under various machining conditions were utilized to monitor the changes in tool conditions. A comparison between the ResNet and LSTM tool wear prediction models indicated that the proposed ResNet-LSTM model significantly improved the prediction accuracy compared to the individual LSTM and ResNet models. Moreover, ResNet-LSTM exhibited adaptive noise reduction capabilities at the front end of the network for signal feature extraction, thereby enhancing the signal feature extraction capability. The ResNet-LSTM model yielded an average prediction error of 0.0085 mm and a tool wear prediction accuracy of 98.25%. These results validate the feasibility of the tool wear prediction method proposed in this study.
引用
收藏
页数:24
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