Intelligent tool wear monitoring and multi-step prediction based on deep learning model
出版年份 2021 全文链接
标题
Intelligent tool wear monitoring and multi-step prediction based on deep learning model
作者
关键词
Feature normalization, Attention mechanism, Tool wear monitoring, Multi-step prediction, Deep learning
出版物
JOURNAL OF MANUFACTURING SYSTEMS
Volume 62, Issue -, Pages 286-300
出版商
Elsevier BV
发表日期
2021-12-08
DOI
10.1016/j.jmsy.2021.12.002
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism
- (2021) Xingwei Xu et al. MEASUREMENT
- Vibration-based tool condition monitoring in milling of Ti-6Al-4V using an optimization model of GM(1,N) and SVM
- (2021) Kaki Venkata Rao et al. The International Journal of Advanced Manufacturing Technology
- Physics-guided logistic classification for tool life modeling and process parameter optimization in machining
- (2021) Jaydeep Karandikar et al. JOURNAL OF MANUFACTURING SYSTEMS
- The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation
- (2021) Mohamed Lamine Bouhalais et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Deep ensemble learning-based approach to real-time power system state estimation
- (2021) Narayan Bhusal et al. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
- Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network
- (2021) Xianli Liu et al. JOURNAL OF MANUFACTURING SYSTEMS
- Temporal convolutional network with soft thresholding and attention mechanism for machinery prognostics
- (2021) Yiwei Wang et al. JOURNAL OF MANUFACTURING SYSTEMS
- Multiscale attentional residual neural network framework for remaining useful life prediction of bearings
- (2021) Wen Yu et al. MEASUREMENT
- Prediction of surface residual stress in end milling with Gaussian process regression
- (2021) Minghui Cheng et al. MEASUREMENT
- Research on tool wear prediction based on temperature signals and deep learning
- (2021) Zhaopeng He et al. WEAR
- Tool wear monitoring of TC4 titanium alloy milling process based on multi-channel signal and time-dependent properties by using deep learning
- (2021) Boling Yan et al. JOURNAL OF MANUFACTURING SYSTEMS
- Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems
- (2021) Seulki Han et al. JOURNAL OF MANUFACTURING SYSTEMS
- Physics-informed meta learning for machining tool wear prediction
- (2021) Yilin Li et al. JOURNAL OF MANUFACTURING SYSTEMS
- A hybrid information model based on long short-term memory network for tool condition monitoring
- (2020) Weili Cai et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Data-driven prognosis method using hybrid deep recurrent neural network
- (2020) Min Xia et al. APPLIED SOFT COMPUTING
- A tool wear monitoring and prediction system based on multiscale deep learning models and fog computing
- (2020) Huihui Qiao et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Effect of machining parameters on surface roughness for compacted graphite cast iron by analyzing covariance function of Gaussian process regression
- (2020) Juan Lu et al. MEASUREMENT
- Intelligent Prognostics of Machining Tools Based on Adaptive Variational Mode Decomposition and Deep Learning Method with Attention Mechanism
- (2020) Chongdang Liu et al. NEUROCOMPUTING
- In-process tool condition forecasting based on a deep learning method
- (2020) Huibin Sun et al. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
- A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders
- (2020) Aniekan Essien et al. IEEE Transactions on Industrial Informatics
- Physics guided neural network for machining tool wear prediction
- (2020) Jinjiang Wang et al. JOURNAL OF MANUFACTURING SYSTEMS
- A CNN-BiLSTM-AM method for stock price prediction
- (2020) Wenjie Lu et al. NEURAL COMPUTING & APPLICATIONS
- Extreme learning machine ensemble model for time series forecasting boosted by PSO: Application to an electric consumption problem
- (2020) Mikel Larrea et al. NEUROCOMPUTING
- A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning
- (2019) Yingguang Li et al. CIRP ANNALS-MANUFACTURING TECHNOLOGY
- Research on the Milling Tool Wear and Life Prediction by Establishing an Integrated Predictive Model
- (2019) Yinfei Yang et al. MEASUREMENT
- Remaining useful life prediction based on health index similarity
- (2019) Yingchao Liu et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- Deep heterogeneous GRU model for predictive analytics in smart manufacturing: Application to tool wear prediction
- (2019) Jinjiang Wang et al. COMPUTERS IN INDUSTRY
- Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system
- (2018) Jun Wu et al. APPLIED SOFT COMPUTING
- Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks
- (2018) Rui Zhao et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Real-time cutting tool state recognition approach based on machining features in NC machining process of complex structural parts
- (2018) Changqing Liu et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Gaussian process regression for tool wear prediction
- (2018) Dongdong Kong et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Machinery health prognostics: A systematic review from data acquisition to RUL prediction
- (2018) Yaguo Lei et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Short-term Load Forecasting with Deep Residual Networks
- (2018) Kunjin Chen et al. IEEE Transactions on Smart Grid
- In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm
- (2018) Vigneashwara Pandiyan et al. Journal of Manufacturing Processes
- A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series
- (2018) Huihui Qiao et al. SENSORS
- Deep learning and its applications to machine health monitoring
- (2018) Rui Zhao et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission
- (2017) S.A. Aye et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks
- (2017) Rui Zhao et al. SENSORS
- A comprehensive study on the effects of tool wear on surface roughness, dimensional integrity and residual stress in turning IN718 hard-to-machine alloy
- (2017) Farbod Akhavan Niaki et al. Journal of Manufacturing Processes
- Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model
- (2016) Kamran Javed et al. JOURNAL OF INTELLIGENT MANUFACTURING
- State of health monitoring in machining: Extended Kalman filter for tool wear assessment in turning of IN718 hard-to-machine alloy
- (2016) Farbod Akhavan Niaki et al. Journal of Manufacturing Processes
- Tool life predictions in milling using spindle power with the neural network technique
- (2016) Cyril Drouillet et al. Journal of Manufacturing Processes
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