Related references
Note: Only part of the references are listed.Predicting tool wear size across multi-cutting conditions using advanced machine learning techniques
Yan Shen et al.
JOURNAL OF INTELLIGENT MANUFACTURING (2021)
Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning
Mustafa Kuntoglu et al.
MEASUREMENT (2021)
A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends
Mustafa Kuntoglu et al.
SENSORS (2021)
Tool wear state prediction based on feature-based transfer learning
Jianbo Li et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)
Heterogeneous sensors-based feature optimisation and deep learning for tool wear prediction
Xiaoyang Zhang et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)
A state-of-the-art review on sensors and signal processing systems in mechanical machining processes
Mustafa Kuntoglu et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)
Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations
Zhiwen Huang et al.
JOURNAL OF INTELLIGENT MANUFACTURING (2020)
Tool condition monitoring techniques in milling process - a review
T. Mohanraj et al.
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T (2020)
A hybrid information model based on long short-term memory network for tool condition monitoring
Weili Cai et al.
JOURNAL OF INTELLIGENT MANUFACTURING (2020)
A tool wear monitoring and prediction system based on multiscale deep learning models and fog computing
Huihui Qiao et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)
Review of tool condition monitoring in machining and opportunities for deep learning
G. Serin et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)
Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations
Min Liu et al.
SENSORS (2020)
A Qualitative Tool Condition Monitoring Framework Using Convolution Neural Network and Transfer Learning
Harshavardhan Mamledesai et al.
APPLIED SCIENCES-BASEL (2020)
A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling
Yuqing Zhou et al.
MEASUREMENT (2020)
Milling Tool Wear Prediction Method Based on Deep Learning Under Variable Working Conditions
Mingwei Wang et al.
IEEE ACCESS (2020)
Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing
Chuang Sun et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)
Research on the milling tool wear and life prediction by establishing an integrated predictive model
Yinfei Yang et al.
MEASUREMENT (2019)
Tool wear state recognition based on GWO-SVM with feature selection of genetic algorithm
Xiaoping Liao et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2019)
Investigation of progressive tool wear for determining of optimized machining parameters in turning
Mustafa Kuntoglu et al.
MEASUREMENT (2019)
Estimation of Remaining Useful Life of Rolling Element Bearings Using Wavelet Packet Decomposition and Artificial Neural Network
Abbas Rohani Bastami et al.
IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING (2019)
Time varying and condition adaptive hidden Markov model for tool wear state estimation and remaining useful life prediction in micro-milling
Weijian Li et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)
Mutual Information-Dynamic Stacked Sparse Autoencoders for Fault Detection
Jie Yin et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2019)
Multisensory Data-Driven Health Degradation Monitoring of Machining Tools by Generalized Multiclass Support Vector Machine
Yiwei Cheng et al.
IEEE ACCESS (2019)
A Generalized Multisensor Real-Time Tool Condition-Monitoring Approach Using Deep Recurrent Neural Network
M. Hassan et al.
SMART AND SUSTAINABLE MANUFACTURING SYSTEMS (2019)
An embedded multi-sensor system on the rotating dynamometer for real-time condition monitoring in milling
Muhammad Rizal et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2018)
Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system
Jun Wu et al.
APPLIED SOFT COMPUTING (2018)
Review of tool condition monitoring methods in milling processes
Yuqing Zhou et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2018)
Bearing remaining useful life prediction based on deep autoencoder and deep neural networks
Lei Ren et al.
JOURNAL OF MANUFACTURING SYSTEMS (2018)
Gaussian process regression for tool wear prediction
Dongdong Kong et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)
Frequency and Time-Frequency Analysis of Cutting Force and Vibration Signals for Tool Condition Monitoring
Juan C. Jauregui et al.
IEEE ACCESS (2018)
In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm
Vigneashwara Pandiyan et al.
JOURNAL OF MANUFACTURING PROCESSES (2018)
Predicting tool wear with multi-sensor data using deep belief networks
Yuxuan Chen et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2018)
A Multisensor Fusion Method for Tool Condition Monitoring in Milling
Yuqing Zhou et al.
SENSORS (2018)
Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing
Jinjiang Wang et al.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING (2017)
Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks
Rui Zhao et al.
SENSORS (2017)
Multi-sensor data fusion framework for CNC machining monitoring
Joao A. Duro et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2016)
Tool life predictions in milling using spindle power with the neural network technique
Cyril Drouillet et al.
JOURNAL OF MANUFACTURING PROCESSES (2016)
Incremental learning for online tool condition monitoring using Ellipsoid ARTMAP network model
C. Liu et al.
APPLIED SOFT COMPUTING (2015)
Tool life estimation based on acoustic emission monitoring in end-milling of H13 mould-steel
O. Olufayo et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2015)
A method for tool condition monitoring based on sensor fusion
Kai-feng Zhang et al.
JOURNAL OF INTELLIGENT MANUFACTURING (2015)
Health assessment and life prediction of cutting tools based on support vector regression
T. Benkedjouh et al.
JOURNAL OF INTELLIGENT MANUFACTURING (2015)
Cutting forces and tool failure in high-speed milling of titanium alloy TC21 with coated carbide tools
Hongxia Zhang et al.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE (2015)
Correlation study of tool flank wear with machined surface texture in end milling
S. Dutta et al.
MEASUREMENT (2013)
Advanced monitoring of machining operations
R. Teti et al.
CIRP ANNALS-MANUFACTURING TECHNOLOGY (2010)
Share Paper
