A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges
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Title
A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges
Authors
Keywords
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Journal
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-05-31
DOI
10.1007/s00170-021-07325-7
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Note: Only part of the references are listed.- Predictive Maintenance System for Production Lines in Manufacturing: A Machine Learning Approach Using IoT Data in Real-Time
- (2021) Serkan Ayvaz et al. EXPERT SYSTEMS WITH APPLICATIONS
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- (2021) Weicheng Guo et al. The International Journal of Advanced Manufacturing Technology
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- (2021) Vahid Nasir et al. The International Journal of Advanced Manufacturing Technology
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- Tool Wear Online Monitoring Method Based on DT and SSAE-PHMM
- (2021) Xiangyu Zhang et al. JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING
- 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
- An edge-labeling graph neural network method for tool wear condition monitoring using wear image with small samples
- (2021) Gaofeng Zhi et al. MEASUREMENT SCIENCE and TECHNOLOGY
- Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process
- (2021) Nika Brili et al. SENSORS
- Heterogeneous sensors-based feature optimisation and deep learning for tool wear prediction
- (2021) Xiaoyang Zhang et al. The International Journal of Advanced Manufacturing Technology
- Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling
- (2021) Mohsen Marani et al. MEASUREMENT
- A chatter detection method in milling of thin-walled TC4 alloy workpiece based on auto-encoding and hybrid clustering
- (2021) Yichao Dun et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Research on tool wear prediction based on temperature signals and deep learning
- (2021) Zhaopeng He et al. WEAR
- An optimized convolutional neural network for chatter detection in the milling of thin-walled parts
- (2020) Weiguo Zhu et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Modeling and analysis of tool wear prediction based on SVD and BiLSTM
- (2020) Xiaoqiang Wu et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Deep neural network-based cost function for metal cutting data assimilation
- (2020) Takashi Misaka et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Milling chatter detection using scalogram and deep convolutional neural network
- (2020) Minh-Quang Tran et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- A hybrid information model based on long short-term memory network for tool condition monitoring
- (2020) Weili Cai et al. JOURNAL OF INTELLIGENT MANUFACTURING
- A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network
- (2020) Qinglong An et al. MEASUREMENT
- A novel transformer-based neural network model for tool wear estimation
- (2020) Hui Liu et al. MEASUREMENT SCIENCE and TECHNOLOGY
- Transfer learning for enhanced machine fault diagnosis in manufacturing
- (2020) Peng Wang et al. CIRP ANNALS-MANUFACTURING TECHNOLOGY
- Intelligent wood machining monitoring using vibration signals combined with self-organizing maps for automatic feature selection
- (2020) Vahid Nasir et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Machine learning and data analytics for the IoT
- (2020) Erwin Adi et al. NEURAL COMPUTING & APPLICATIONS
- A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring
- (2020) Jiayu Ou et al. SENSORS
- Stacked Auto-Encoder Based CNC Tool Diagnosis Using Discrete Wavelet Transform Feature Extraction
- (2020) Jonggeun Kim et al. Processes
- 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
- Intelligent recognition of milling cutter wear state with cutting parameter independence based on deep learning of spindle current clutter signal
- (2020) Kaiyu Song et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Characterization, optimization, and acoustic emission monitoring of airborne dust emission during wood sawing
- (2020) Vahid Nasir et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Online chatter detection for milling operations using LSTM neural networks assisted by motor current signals of ball screw drives
- (2020) Rajiv Kumar Vashisht et al. JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME
- Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network
- (2020) Yung-Chih Lin et al. Applied Sciences-Basel
- Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems
- (2020) Cristina Morariu et al. COMPUTERS IN INDUSTRY
- Knowledge-based deep belief network for machining roughness prediction and knowledge discovery
- (2020) Jianbo Yu et al. COMPUTERS IN INDUSTRY
- Evaluation of turned and milled surfaces roughness using convolutional neural network
- (2020) Achmad P. Rifai et al. MEASUREMENT
- Tool wear mechanism and prediction in milling TC18 titanium alloy using deep learning
- (2020) Junyan Ma et al. MEASUREMENT
- A Qualitative Tool Condition Monitoring Framework Using Convolution Neural Network and Transfer Learning
- (2020) Harshavardhan Mamledesai et al. Applied Sciences-Basel
- In-process tap tool wear monitoring and prediction using a novel model based on deep learning
- (2020) Xingwei Xu et al. The International Journal of Advanced Manufacturing Technology
- Intelligent analysis of tool wear state using stacked denoising autoencoder with online sequential-extreme learning machine
- (2020) Jiayu Ou et al. MEASUREMENT
- Intelligent monitoring and diagnostics using a novel integrated model based on deep learning and multi-sensor feature fusion
- (2020) Xingwei Xu et al. MEASUREMENT
- Acoustic emission monitoring of sawing process: artificial intelligence approach for optimal sensory feature selection
- (2019) Vahid Nasir et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Fundamentals of smart manufacturing: A multi-thread perspective
- (2019) Andrew Kusiak ANNUAL REVIEWS IN CONTROL
- Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification
- (2019) Xin-Cheng Cao et al. COMPUTERS IN INDUSTRY
- Optimal power consumption and surface quality in the circular sawing process of Douglas-fir wood
- (2019) Vahid Nasir et al. European Journal of Wood and Wood Products
- A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
- (2019) Yong Yu et al. NEURAL COMPUTATION
- Tool wear classification using time series imaging and deep learning
- (2019) Giovanna Martínez-Arellano et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- A comprehensive review on minimum quantity lubrication (MQL) in machining processes using nano-cutting fluids
- (2019) Zafar Said et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- The use of support vector machine, neural network, and regression analysis to predict and optimize surface roughness and cutting forces in milling
- (2019) Ali Yeganefar et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Tool remaining useful life prediction method based on LSTM under variable working conditions
- (2019) Jing-Tao Zhou et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations
- (2019) Zhiwen Huang et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme
- (2019) Wennian Yu et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
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- (2019) Xuefeng Wu et al. SENSORS
- An Intelligent Milling Tool Wear Monitoring Methodology Based on Convolutional Neural Network with Derived Wavelet Frames Coefficient
- (2019) Xincheng Cao et al. Applied Sciences-Basel
- Deep heterogeneous GRU model for predictive analytics in smart manufacturing: Application to tool wear prediction
- (2019) Jinjiang Wang et al. COMPUTERS IN INDUSTRY
- Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery
- (2019) Biao Wang et al. NEUROCOMPUTING
- Research on a Real-Time Monitoring Method for the Wear State of a Tool Based on a Convolutional Bidirectional LSTM Model
- (2019) Chen et al. Symmetry-Basel
- An unsupervised online monitoring method for tool wear using a sparse auto-encoder
- (2019) Jianming Dou et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- A novel scalable method for machine degradation assessment using deep convolutional neural network
- (2019) Pin Li et al. MEASUREMENT
- Tool Wear Prediction via Multidimensional Stacked Sparse Autoencoders With Feature Fusion
- (2019) Chengming Shi et al. IEEE Transactions on Industrial Informatics
- Deep learning for healthcare applications based on physiological signals: A review
- (2018) Oliver Faust et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
- (2018) Guoqian Jiang et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Influences of tool structure, tool material and tool wear on machined surface integrity during turning and milling of titanium and nickel alloys: a review
- (2018) Bing Wang et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Cloud-based manufacturing process monitoring for smart diagnosis services
- (2018) Alessandra Caggiano INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
- An approach to cleaner production for machining hardened steel using different cooling-lubrication conditions
- (2018) Mozammel Mia et al. JOURNAL OF CLEANER PRODUCTION
- Towards sustainability assessment of machining processes
- (2018) H.A. Hegab et al. JOURNAL OF CLEANER PRODUCTION
- Deep learning for smart manufacturing: Methods and applications
- (2018) Jinjiang Wang et al. JOURNAL OF MANUFACTURING SYSTEMS
- Data-driven smart manufacturing
- (2018) Fei Tao et al. JOURNAL OF MANUFACTURING SYSTEMS
- A review on the application of deep learning in system health management
- (2018) Samir Khan et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- An overview on Restricted Boltzmann Machines
- (2018) Nan Zhang et al. NEUROCOMPUTING
- Using multiple feature spaces-based deep learning for tool condition monitoring in ultra-precision manufacturing
- (2018) Chengming Shi et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Predicting tool wear with multi-sensor data using deep belief networks
- (2018) Yuxuan Chen et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series
- (2018) Huihui Qiao et al. SENSORS
- Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry
- (2018) Dong-Hyeon Kim et al. International Journal of Precision Engineering and Manufacturing-Green Technology
- Deep learning and its applications to machine health monitoring
- (2018) Rui Zhao et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing
- (2018) Chuang Sun et al. IEEE Transactions on Industrial Informatics
- Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0
- (2018) Alberto Diez-Olivan et al. Information Fusion
- State of The Art-Intense Review on Artificial Intelligence Systems Application in Process Planning and Manufacturing
- (2017) S.P. Leo Kumar ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- Condition monitoring towards energy-efficient manufacturing: a review
- (2017) Zude Zhou et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Smart manufacturing must embrace big data
- (2017) Andrew Kusiak NATURE
- Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks
- (2017) Rui Zhao et al. SENSORS
- DISTINGUISHING SENSOR FAULTS FROM SYSTEM FAULTS BY UTILIZING MINIMUM SENSOR REDUNDANCY
- (2017) Morteza Taiebat et al. TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING
- Minimum Quantity Lubrication and Carbon Footprint: A Step towards Sustainability
- (2017) Muhammad Omair et al. Sustainability
- Tool-Wear Analysis Using Image Processing of the Tool Flank
- (2017) Ovidiu Moldovan et al. Symmetry-Basel
- Applications of artificial intelligence in intelligent manufacturing: a review
- (2017) Bo-hu Li et al. Frontiers of Information Technology & Electronic Engineering
- A novel simulation method for interaction of machining process and machine tool structure
- (2016) Wanqun Chen et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- A novel data transformation model for small data-set learning
- (2016) Der-Chiang Li et al. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
- Tool wear predictability estimation in milling based on multi-sensorial data
- (2015) P. Stavropoulos et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Monitoring the tool wear, surface roughness and chip formation occurrences using multiple sensors in turning
- (2014) M.S.H. Bhuiyan et al. JOURNAL OF MANUFACTURING SYSTEMS
- Monitoring and processing signal applied in machining processes – A review
- (2014) C.H. Lauro et al. MEASUREMENT
- A survey on feature selection methods
- (2013) Girish Chandrashekar et al. COMPUTERS & ELECTRICAL ENGINEERING
- Representation Learning: A Review and New Perspectives
- (2013) Y. Bengio et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- CHMM for tool condition monitoring and remaining useful life prediction
- (2011) Mei Wang et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Chatter in machining processes: A review
- (2011) Guillem Quintana et al. INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE
- Advanced monitoring of machining operations
- (2010) R. Teti et al. CIRP ANNALS-MANUFACTURING TECHNOLOGY
- Quality and Inspection of Machining Operations: Tool Condition Monitoring
- (2010) John T. Roth et al. JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME
- Interaction of manufacturing process and machine tool
- (2009) C. Brecher et al. CIRP ANNALS-MANUFACTURING TECHNOLOGY
- A review of machining monitoring systems based on artificial intelligence process models
- (2009) Jose Vicente Abellan-Nebot et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results
- (2009) Kunpeng Zhu et al. INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE
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