Intelligent tool wear monitoring and multi-step prediction based on deep learning model

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