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A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges
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ON-LINE AND INDIRECT TOOL WEAR MONITORING IN TURNING WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW OF MORE THAN A DECADE OF RESEARCH
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Interpreting Random Forest Classification Models Using a Feature Contribution Method
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Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life
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A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis
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Neural Networks: Tricks of the Trade
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Optimal power consumption and surface quality in the circular sawing process of Douglas-fir wood
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A review of machining monitoring systems based on artificial intelligence process models
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CHMM for tool condition monitoring and remaining useful life prediction
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Tool wear predictability estimation in milling based on multi-sensorial data
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A novel simulation method for interaction of machining process and machine tool structure
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Condition monitoring towards energy-efficient manufacturing: a review
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Influences of tool structure, tool material and tool wear on machined surface integrity during turning and milling of titanium and nickel alloys: a review
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Predicting tool wear with multi-sensor data using deep belief networks
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Acoustic emission monitoring of sawing process: artificial intelligence approach for optimal sensory feature selection
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Tool wear classification using time series imaging and deep learning
inferredcites →https://doi.org/10.1007/s00170-019-04227-7openalex-snapshot
The use of support vector machine, neural network, and regression analysis to predict and optimize surface roughness and cutting forces in milling
inferredcites →https://doi.org/10.1007/s00170-019-04349-yopenalex-snapshot
Tool remaining useful life prediction method based on LSTM under variable working conditions
inferredcites →https://doi.org/10.1007/s00170-019-04382-xopenalex-snapshot
A comprehensive review on minimum quantity lubrication (MQL) in machining processes using nano-cutting fluids
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An unsupervised online monitoring method for tool wear using a sparse auto-encoder
inferredcites →https://doi.org/10.1007/s00170-019-04807-7openalex-snapshot
Milling chatter detection using scalogram and deep convolutional neural network
inferredcites →https://doi.org/10.1007/s00170-019-04899-1openalex-snapshot
An optimized convolutional neural network for chatter detection in the milling of thin-walled parts
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Modeling and analysis of tool wear prediction based on SVD and BiLSTM
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Deep neural network-based cost function for metal cutting data assimilation
inferredcites →https://doi.org/10.1007/s00170-020-05449-wopenalex-snapshot
Review of tool condition monitoring in machining and opportunities for deep learning
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Intelligent wood machining monitoring using vibration signals combined with self-organizing maps for automatic feature selection
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A tool wear monitoring and prediction system based on multiscale deep learning models and fog computing
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Intelligent recognition of milling cutter wear state with cutting parameter independence based on deep learning of spindle current clutter signal
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Characterization, optimization, and acoustic emission monitoring of airborne dust emission during wood sawing
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Dimension reduction and 2D-visualization for early change of state detection in a machining process with a variational autoencoder approach
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In-process tap tool wear monitoring and prediction using a novel model based on deep learning
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Prediction of the remaining useful life of cutting tool using the Hurst exponent and CNN-LSTM
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Cutting tool temperature monitoring in circular sawing: measurement and multi-sensor feature fusion-based prediction
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Prediction of surface roughness based on a hybrid feature selection method and long short-term memory network in grinding
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Heterogeneous sensors-based feature optimisation and deep learning for tool wear prediction
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Machine learning and data analytics for the IoT
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Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations
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A hybrid information model based on long short-term memory network for tool condition monitoring
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Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry
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CNN based tool monitoring system to predict life of cutting tool
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Artificial intelligence in monitoring and the mechanics of machining
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Fundamentals of smart manufacturing: A multi-thread perspective
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Interaction of manufacturing process and machine tool
inferredcites →https://doi.org/10.1016/j.cirp.2010.05.010openalex-snapshot
Advanced monitoring of machining operations
inferredcites →https://doi.org/10.1016/j.cirp.2020.04.074openalex-snapshot
Transfer learning for enhanced machine fault diagnosis in manufacturing
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Deep learning for healthcare applications based on physiological signals: A review
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A survey on feature selection methods
inferredcites →https://doi.org/10.1016/j.compind.2018.12.018openalex-snapshot
Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification
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Deep heterogeneous GRU model for predictive analytics in smart manufacturing: Application to tool wear prediction
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Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems
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RETRACTED: Knowledge-based deep belief network for machining roughness prediction and knowledge discovery
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State of The Art-Intense Review on Artificial Intelligence Systems Application in Process Planning and Manufacturing
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Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time
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New Approach based on Autoencoders to Monitor the Tool Wear Condition in HSM
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Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results
inferredcites →https://doi.org/10.1016/j.ijmachtools.2011.01.001openalex-snapshot
Chatter in machining processes: A review
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Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0
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Towards sustainability assessment of machining processes
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An approach to cleaner production for machining hardened steel using different cooling-lubrication conditions
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Effect of pine impregnation and feed speed on sound level and cutting power in wood sawing
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Monitoring the tool wear, surface roughness and chip formation occurrences using multiple sensors in turning
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Deep learning for smart manufacturing: Methods and applications
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Data-driven smart manufacturing
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Monitoring and processing signal applied in machining processes – A review
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A novel scalable method for machine degradation assessment using deep convolutional neural network
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A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network
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Evaluation of turned and milled surfaces roughness using convolutional neural network
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Intelligent monitoring and diagnostics using a novel integrated model based on deep learning and multi-sensor feature fusion
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Intelligent analysis of tool wear state using stacked denoising autoencoder with online sequential-extreme learning machine
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Tool wear mechanism and prediction in milling TC18 titanium alloy using deep learning
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Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism
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Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling
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Industrial Artificial Intelligence for industry 4.0-based manufacturing systems
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Sensor fusion and random forest modeling for identifying frozen and green wood during lumber manufacturing
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An overview on Restricted Boltzmann Machines
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Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery
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Analysis of Feature Extracting Ability for Cutting State Monitoring Using Deep Belief Networks
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In-process Tool Wear Prediction System Based on Machine Learning Techniques and Force Analysis
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Research on tool wear prediction based on temperature signals and deep learning
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A review on the application of deep learning in system health management
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Deep learning and its applications to machine health monitoring
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Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme
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A chatter detection method in milling of thin-walled TC4 alloy workpiece based on auto-encoding and hybrid clustering
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Neural Network Sensor Fusion for Tool Condition Monitoring
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Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography
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Neural network solutions to the tool condition monitoring problem in metal cutting—A critical review of methods
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Artificial intelligence approaches to determination of CNC machining parameters in manufacturing: a review
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Smart manufacturing must embrace big data
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A novel data transformation model for small data-set learning
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Cloud-based manufacturing process monitoring for smart diagnosis services
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A review on wood machining: characterization, optimization, and monitoring of the sawing process
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The effect of rotation speed on the power consumption and cutting accuracy of guided circular saw: Experimental measurement and analysis of saw critical and flutter speeds
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Machine learning in manufacturing: advantages, challenges, and applications
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A novel transformer-based neural network model for tool wear estimation
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An edge-labeling graph neural network method for tool wear condition monitoring using wear image with small samples
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Deep learning for healthcare: review, opportunities and challenges
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An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions
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Tool Wear Predicting Based on Multisensory Raw Signals Fusion by Reshaped Time Series Convolutional Neural Network in Manufacturing
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An Intelligent System for Grinding Wheel Condition Monitoring Based on Machining Sound and Deep Learning
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Applications of Internet of Things in manufacturing
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A Deep Learning Approach for High Speed Machining Tool Wear Monitoring
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Machine health monitoring with LSTM networks
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Looking inside the ANN "black box": classifying individual neurons as outlier detectors
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A CNN-based Transfer Learning Method for Defect Classification in Semiconductor Manufacturing
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Intelligent Machining Monitoring Using Sound Signal Processed With the Wavelet Method and a Self-Organizing Neural Network
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The Internet of Things in Manufacturing: Key Issues and Potential Applications
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Remaining Useful Life Prediction of Machining Tools by 1D-CNN LSTM Network
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Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
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Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprecision Manufacturing
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Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing
inferredcites →https://doi.org/10.1109/tii.2019.2949355openalex-snapshot
Tool Wear Prediction via Multidimensional Stacked Sparse Autoencoders With Feature Fusion
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Representation Learning: A Review and New Perspectives
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A CNN-Based Transfer Learning Method for Defect Classification in Semiconductor Manufacturing
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Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring
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An Approach for the Construction of Entropy Measure and Energy Map in Machine Fault Diagnosis
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Quality and Inspection of Machining Operations: Tool Condition Monitoring
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Online Chatter Detection for Milling Operations Using LSTM Neural Networks Assisted by Motor Current Signals of Ball Screw Drives
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Tool Wear Online Monitoring Method Based on DT and SSAE-PHMM
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DISTINGUISHING SENSOR FAULTS FROM SYSTEM FAULTS BY UTILIZING MINIMUM SENSOR REDUNDANCY
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Performance of Informative Wavelets for Classification and Diagnosis of Machine Faults
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Remaining Useful Life Prediction of Cutting Tools based on Deep Adversarial Transfer Learning
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Minimization of Surface Roughness and Tool Vibration in CNC Milling Operation
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A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
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Internet of things enabled manufacturing: a review
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Self-supervised learning for tool wear monitoring with a disentangled-variational-autoencoder
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Self-supervised learning for tool wear monitoring with a disentangled-variational-autoencoder
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Cloud manufacturing: a service-oriented manufacturing paradigm. A review paper
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Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning
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Applications of artificial intelligence in intelligent manufacturing: a review
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Design of Contactlessly Powered and Piezoelectrically Actuated Tools for Non-Resonant Vibration Assisted Milling
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Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network
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A Qualitative Tool Condition Monitoring Framework Using Convolution Neural Network and Transfer Learning
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An Intelligent Milling Tool Wear Monitoring Methodology Based on Convolutional Neural Network with Derived Wavelet Frames Coefficient
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Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach
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Stacked Auto-Encoder Based CNC Tool Diagnosis Using Discrete Wavelet Transform Feature Extraction
inferredcites →https://doi.org/10.3390/s17020273openalex-snapshot
Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks
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A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series
inferredcites →https://doi.org/10.3390/s19183817openalex-snapshot
Automatic Identification of Tool Wear Based on Convolutional Neural Network in Face Milling Process
inferredcites →https://doi.org/10.3390/s20102878openalex-snapshot
A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring
inferredcites →https://doi.org/10.3390/s21051917openalex-snapshot
Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process
inferredcites →https://doi.org/10.3390/su9050714openalex-snapshot
Minimum Quantity Lubrication and Carbon Footprint: A Step towards Sustainability
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Research on a Real-Time Monitoring Method for the Wear State of a Tool Based on a Convolutional Bidirectional LSTM Model
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Tool-Wear Analysis Using Image Processing of the Tool Flank
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Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks
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Determination of Over-Learning and Over-Fitting Problem in Back Propagation Neurl Network
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inferred← citeshttps://doi.org/10.1007/978-981-97-5775-6_12openalex-snapshot
Status Quo, Advances and Futures of Machine Learning in Fault Detection and Diagnosis for Energy: A Review
inferred← citeshttps://doi.org/10.1007/s00107-022-01794-7openalex-snapshot
Predicting moisture content in kiln dried timbers using machine learning
inferred← citeshttps://doi.org/10.1007/s00107-024-02139-2openalex-snapshot
Sound classification and power consumption to sound intensity relation as a tool for wood machining monitoring
inferred← citeshttps://doi.org/10.1007/s00107-026-02465-7openalex-snapshot
Acoustic signal-based cutting tool condition monitoring in woodworking using deep learning
inferred← citeshttps://doi.org/10.1007/s00170-021-07784-yopenalex-snapshot
Cutting tool prognostics enabled by hybrid CNN-LSTM with transfer learning
inferred← citeshttps://doi.org/10.1007/s00170-021-08047-6openalex-snapshot
Technology development and commercial applications of industrial fault diagnosis system: a review
inferred← citeshttps://doi.org/10.1007/s00170-021-08163-3openalex-snapshot
Job shop rescheduling with rework and reconditioning in Industry 4.0: an event-driven approach
inferred← citeshttps://doi.org/10.1007/s00170-022-08957-zopenalex-snapshot
Cutting temperature measurement and prediction in machining processes: comprehensive review and future perspectives
inferred← citeshttps://doi.org/10.1007/s00170-022-09452-1openalex-snapshot
Transfer fault prognostic for rolling bearings across different working conditions: a domain adversarial perspective
inferred← citeshttps://doi.org/10.1007/s00170-022-10017-5openalex-snapshot
A milling cutter state recognition method based on multi-source heterogeneous data fusion
inferred← citeshttps://doi.org/10.1007/s00170-022-10018-4openalex-snapshot
A distributed model predictive control with machine learning for automated shot peening machine in remanufacturing processes
inferred← citeshttps://doi.org/10.1007/s00170-022-10335-8openalex-snapshot
A new lightweight deep neural network for surface scratch detection
inferred← citeshttps://doi.org/10.1007/s00170-022-10455-1openalex-snapshot
Tool wear prediction based on convolutional bidirectional LSTM model with improved particle swarm optimization
inferred← citeshttps://doi.org/10.1007/s00170-023-10969-2openalex-snapshot
Chatter detection in milling processes—a review on signal processing and condition classification
inferred← citeshttps://doi.org/10.1007/s00170-023-10970-9openalex-snapshot
Enhanced safety implementation in 5S + 1 via object detection algorithms
inferred← citeshttps://doi.org/10.1007/s00170-023-11832-0openalex-snapshot
Tool wear prediction based on parallel dual-channel adaptive feature fusion
inferred← citeshttps://doi.org/10.1007/s00170-023-12797-wopenalex-snapshot
Tool wear classification based on maximal overlap discrete wavelet transform and hybrid deep learning model
inferred← citeshttps://doi.org/10.1007/s00170-024-13055-3openalex-snapshot
A novel method based on deep transfer learning for tool wear state prediction under cross-dataset
inferred← citeshttps://doi.org/10.1007/s00170-024-13228-0openalex-snapshot
Hyperparameter optimization strategy of multilayer perceptron model for injection molding quality prediction
inferred← citeshttps://doi.org/10.1007/s00170-024-13364-7openalex-snapshot
Physics-informed interpretable machine learning method for DOC monitoring in peripheral milling
inferred← citeshttps://doi.org/10.1007/s00170-024-13492-0openalex-snapshot
An experimental hybrid customized AI and generative AI chatbot human machine interface to improve a factory troubleshooting downtime in the context of Industry 5.0
inferred← citeshttps://doi.org/10.1007/s00170-024-13884-2openalex-snapshot
Cutting model integrated digital twin–based process monitoring in small-batch machining
inferred← citeshttps://doi.org/10.1007/s00170-024-13907-yopenalex-snapshot
Material removal profile and surface topography prediction of ultrasonic vibration-assisted polished based on gray wolf optimization neural network
inferred← citeshttps://doi.org/10.1007/s00170-024-14273-5openalex-snapshot
Research progress on intelligent monitoring of tool condition based on deep learning
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Current state and emerging trends in advanced manufacturing: smart systems
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Towards Frugal Industrial AI: a framework for the development of scalable and robust machine learning models in the shop floor
inferred← citeshttps://doi.org/10.1007/s00170-024-14847-3openalex-snapshot
Smart tool wear state and chatter onset identification system for legacy manual drilling machine operators
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State-of-art, challenges, and outlook on deep hole boring: chatter suppression, tool wear monitoring, and error measurement
inferred← citeshttps://doi.org/10.1007/s00170-025-15174-xopenalex-snapshot
Bayesian monitoring of machining processes using non-intrusive sensing and on-machine comparator measurement
inferred← citeshttps://doi.org/10.1007/s00170-025-15398-xopenalex-snapshot
Semi-supervised prediction of milling cutter wear based on an empirical formula for cutting force and wear
inferred← citeshttps://doi.org/10.1007/s00170-025-15733-2openalex-snapshot
A sustainable maintenance method for the machining process in a triple bottom line concept using the active optimization method
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A tool wear monitoring based on parallel residual channel attention network
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A reliable real-time tool wear monitoring framework based on temporal segmentation using domain-informed stacked AI models with physics-constrained predictions
inferred← citeshttps://doi.org/10.1007/s00170-026-18427-5openalex-snapshot
Predictive maintenance for additive manufacturing through integrated lean six sigma and artificial intelligence approaches
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Machine learning for defect detection and prediction of product and process quality in manufacturing context
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Deep learning-based prediction of thermal behavior in aluminum under nanosecond laser irradiation
inferred← citeshttps://doi.org/10.1007/s10342-021-01426-4openalex-snapshot
Genetic variation analysis and comprehensive evaluation of wood property traits of 20-year-old Chinese fir clone
inferred← citeshttps://doi.org/10.1007/s10489-023-04793-0openalex-snapshot
A holistic approach for improving milling machine cutting tool wear prediction
inferred← citeshttps://doi.org/10.1007/s10845-023-02088-2openalex-snapshot
Tool wear condition monitoring across machining processes based on feature transfer by deep adversarial domain confusion network
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Machining tool identification utilizing temporal 3D point clouds
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Building digital-twin virtual machining for milling chatter detection based on VMD, synchro-squeeze wavelet, and pre-trained network CNNs with vibration signals
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Data-driven linear quadratic tracking based temperature control of a big area additive manufacturing system
inferred← citeshttps://doi.org/10.1007/s10845-024-02532-xopenalex-snapshot
Enhanced prediction accuracy in high-speed grinding of brittle materials using advanced machine learning techniques
inferred← citeshttps://doi.org/10.1007/s10845-026-02826-2openalex-snapshot
A review of AI and machine learning applications in manufacturing processes
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Data-driven tool wear monitoring guided by prior knowledge via degenerate manifold structural constraints
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The use of an artificial neural network for assessing tone perception in electric powertrain noise, vibration and harshness
inferred← citeshttps://doi.org/10.1007/s11119-023-10022-yopenalex-snapshot
Predicting the ripening time of ‘Hass’ and ‘Shepard’ avocado fruit by hyperspectral imaging
inferred← citeshttps://doi.org/10.1007/s11431-023-2615-4openalex-snapshot
Deep-learning-assisted online surface roughness monitoring in ultraprecision fly cutting
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Application of Machine Learning and Deep Learning in Finite Element Analysis: A Comprehensive Review
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Automated manufacturing of large-diameter bolted spherical joints in spatial grid structures using a developed HBSMT CNC machine tool
inferred← citeshttps://doi.org/10.1007/s12065-023-00872-wopenalex-snapshot
From assistive technology to the backbone: the impact of blockchain in manufacturing
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Tool wear classification based on minimalism in deep learning for VanillaNet and recurrence plot encoding technology
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Sustainable Machining of Nimonic 80A: Machine Learning-Driven Optimization of Energy Consumption Under Environmentally Conscious Cooling Strategies
inferred← citeshttps://doi.org/10.1007/s40430-023-04425-1openalex-snapshot
Machine learning and sensor-based approach for defect detection in MEX additive manufacturing process- A Review
inferred← citeshttps://doi.org/10.1007/s40430-024-05097-1openalex-snapshot
Innovative tool condition classification: utilizing time–frequency moments as inputs for BiLSTM networks in milling processes
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Review of tool condition monitoring in milling of thin-walled parts based on digital twin technology
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AI for tribology: Present and future
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Recent Progress of Chatter Detection and Tool Wear Online Monitoring in Machining Process: A Review and Future Prospects
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Remaining Useful Life Prediction of Face Milling Cutter via Vibration Signatures and CNN–BiLSTM Architecture
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Hybrid Deep Learning Based on Domain Expertise and Multimodality for Chatter Detection
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Knowledge flows in industry 4.0 research: a longitudinal and dynamic analysis
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An intelligent surface roughness prediction method based on automatic feature extraction and adaptive data fusion
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Simulation-based resilience analysis in hybrid flow-shop environments
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Transfer learning for spatio-temporal transferability of real-time crash prediction models
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A smartphone-based application for an early skin disease prognosis: Towards a lean healthcare system via computer-based vision
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Toward practical tool wear prediction paradigm with optimized regressive Siamese neural network
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Tool wear state recognition and prediction method based on laplacian eigenmap with ensemble learning model
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Deep learning based automated fracture identification in material characterization experiments
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An innovative multisource multibranch metric ensemble deep transfer learning algorithm for tool wear monitoring
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Intelligent wireless tool wear monitoring system based on chucked tool condition monitoring ring and deep learning
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Additive Manufacturing Modification by Artificial Intelligence, Machine Learning, and Deep Learning: A Review
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Physics-informed neural networks for milling vibration prediction with tri-domain loss
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Remaining useful lifetime prediction for predictive maintenance in manufacturing
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A wavelet-constrained band attention neural network for physics-informed interpretable tool wear prediction
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Surface conditioning in cutting and abrasive processes
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Infrastructure monitoring and quality diagnosis in CNC machining: A review
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Recognition of tool wear in milling using Kolmogorov-Arnold networks and multi-sensor feature fusion
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Machine learning-based prediction of surface checks and bending properties in weathered thermally modified timber
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Acoustic emission monitoring of wood materials and timber structures: A critical review
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A review of strategies, challenges, and ethical implications of machine learning in smart manufacturing
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Wavelet-based hybrid CNN-BiLSTM approach in tool wear monitoring
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Explainable framework based on integrated sensor fusion and deep learning to estimate stability in high-speed micromilling of thin-walled titanium alloy
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A fault evolution knowledge-driven adversarial meta-learning method for few-shot tool state recognition under variable working conditions
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A Computer Vision-Based Quality Assessment Technique for the automatic control of consumables for analytical laboratories
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Typical applications and perspectives of machine learning for advanced precision machining: A comprehensive review
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Domain knowledge integrated CAM system based on multi-objective path optimal planning and deep convolutional neural network
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Multi-condition milling cutter wear prediction based on split-channel information re-fusion and domain adaptation
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Fault diagnosis of a CNC hobbing cutter through machine learning using three axis vibration data
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Systematic review of Cutting Force Measuring Systems in machining: Principles, design, filtering techniques and applications
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Deploying deep convolutional neural network to the battle against cancer: Towards flexible healthcare systems
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A review on semi-supervised learning for EEG-based emotion recognition
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The similarities and differences of multiple chronic diseases risk factors across depressive symptoms trajectories among middle-aged and older Chinese adults: A 10-year longitudinal cohort study
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Review of advanced sensor system applications in grinding operations
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Extending cutting tool remaining life through deep learning and laser shock peening remanufacturing techniques
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Advancing aquifer vulnerability mapping through integrated deep learning approaches
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Model-data hybrid driven approach for remaining useful life prediction of cutting tool based on improved inverse Gaussian process
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Physics-informed inhomogeneous wear identification of end mills by online monitoring data
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Identification of position-related geometric error terms from profile error in four-axis ultra-precision machine tool based on Informer model
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A physics-informed neural network with adaptive loss weighting for tool wear and remaining useful life prediction in deep hole boring
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Unsupervised domain adaptation regression network with efficient channel attention mechanism for tool wear prediction under variable cutting conditions
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Tool wear monitoring strategy during micro-milling of TC4 alloy based on a fusion model of recursive feature elimination-bayesian optimization-extreme gradient boosting
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Advances in acoustic emission monitoring for grinding of hard and brittle materials
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A milling tool wear monitoring method with sensing generalization capability
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Surface roughness prediction through GAN-synthesized power signal as a process signature
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Cutting tool wear state recognition based on a channel-space attention mechanism
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Fault detection and diagnosis of air brake system: A systematic review
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Cross-domain tool wear condition monitoring via residual attention hybrid adaptation network
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Durability and protection of mass timber structures: A review
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Multiscale scattering forests: A domain-generalizing approach for fault diagnosis under data constraints
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Tool wear prediction using multi-domain features and quantised attention embedded CNN
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Prediction of milling force based on spindle current signal by neural networks
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Machine learning and IoT-based approach for tool condition monitoring: A review and future prospects
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Multi-condition wear prediction and assessment of milling cutters based on linear discriminant analysis and ensemble methods
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Study on tool wear state recognition algorithm based on spindle vibration signals collected by homemade tool condition monitoring ring
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Surface roughness prediction based on fusion of dynamic-static data
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New developments and future prospects in commercial tool condition monitoring systems
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Multi-state online monitoring based on smart wireless vibration milling toolholder system
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Position-matched surface roughness prediction of thin-walled parts in milling using the spatial transformer to spindle vibration signals
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How physical information empowers data-driven tool condition monitoring
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Tool wear monitoring by ensemble learning and sensor fusion using power, sound, vibration, and AE signals
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Smart manufacturing with transfer learning under limited data: Towards Data-Driven Intelligences
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Machine learning based assessment of audio signal features for chatter identification in high-speed micromilling of thin-walled TC4 alloy
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Multi-stream big data mining for industry 4.0 in machining: novel application of a Gated Recurrent Unit Network
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Modeling of Deep Learning Applications for Chatter Detection in the Milling Process
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Data-driven long-term condition-based maintenance using anomaly detection under concept drift: A case study of an ultrasonic sieve machine
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Deep Learning Based Tool Wear Estimation Considering Cutting Conditions
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Implicitly labeled Forecasting based Tool Condition Monitoring in Machining Processes
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Enhancing machining efficiency: Integrating machine-learning-based cutting tool condition monitoring in industrial application
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Real-time tool breakage monitoring based on dimensionless indicators under time-varying cutting conditions
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Increasing dynamic accuracy of machine tools using predictive feedforward optimization with hybrid modeling
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Adoptable approaches to predictive maintenance in mining industry: An overview
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A unified multimodal large language model for explainable tool wear state assessment and maintenance decision support
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Machine learning models for prediction and classification of tool wear in sustainable milling of additively manufactured 316 stainless steel
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Tool wear prediction from ultrashort acoustic emission signals: A computationally efficient machine learning approach in Ti6Al4V machining
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A big-data-based recurrent neural network method for forest energy estimation
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Machine learning and artificial intelligence in CNC machine tools, A review
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A novel online tool condition monitoring method for milling titanium alloy with consideration of tool wear law
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Novel tool wear prediction method based on multimodal information fusion and deep subdomain adaptation
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Multi-source signals synthesized via CGAN as fused process signatures for space domain surface topography prediction
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An online monitoring approach of grinding wheel wear condition for robotic weld grinding based on enhanced CNN-GRU model
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A semi-supervised anomaly detection approach for detecting mechanical failures
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A Review on AI-Based Data-Driven Models for Optimization of Nanocarriers as Drug Delivery Systems
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A Novel Spatiotemporal Process Feature Learning Method Based On the Pseudo-Siamese Network for Complex Chemical Process Concurrent Condition Monitoring
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Mineral Texture Classification Using Deep Convolutional Neural Networks: An Application to Zircons From Porphyry Copper Deposits
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Exploring deep learning capabilities for surge predictions in coastal areas
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Prediction of 316 stainless steel low-cycle fatigue life based on machine learning
inferred← citeshttps://doi.org/10.1038/s41598-023-34304-7openalex-snapshot
Automatic resin duct detection and measurement from wood core images using convolutional neural networks
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Prediction of heavy-section ductile iron fracture toughness based on machine learning
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An online monitoring method of milling cutter wear condition driven by digital twin
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Using deep convolutional networks combined with signal processing techniques for accurate prediction of surface quality
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Quality prediction using multiscale convolutional VAEs for thin plate parts
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Closing the loop in next-generation sensing through shadow sphere lithography, plasmonics, and artificial intelligence
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Artificial intelligence in smart drug delivery systems: a step toward personalized medicine
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An experimental anomaly detection framework for a conveyor motor system using recurrent neural network and dendritic gated neural network
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Liquefaction Strength of Ottawa Sand: CDSS Experiments and ANN Modeling
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Deep learning models for intelligent tool condition monitoring and prediction — A review
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Interpretable convolutional neural network for milling cutter wear state recognition
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Research on cross-condition tool wear monitoring methods based on multi-channel fused data and transfer learning
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Accident prediction modeling by artificial neural network in petroleum industry: a case study of National Iranian Oil Products Distribution Company
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Artificial intelligence and its relevance in mechanical engineering from Industry 4.0 perspective
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Concurrence of artificial intelligence and additive manufacturing: a bibliometric analysis
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Identification of wood species and duration of exposure in weathered wood using guided wave propagation
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Human‑centric Industry 5.0 manufacturing: a multi‑level framework from design to consumption within Society 5.0
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Benchmarking moisture prediction in kiln-dried Pacific Coast hemlock wood
inferred← citeshttps://doi.org/10.1088/2631-7990/ad8117openalex-snapshot
Overcoming challenges: advancements in cutting techniques for high strength-toughness alloys in aero-engines
inferred← citeshttps://doi.org/10.1088/2631-8695/ae3459openalex-snapshot
Artificial neural network coupled with machining models for accurate prediction of machining response of magnesium alloy AZ31B: a hybrid model
inferred← citeshttps://doi.org/10.1101/2022.11.16.516774openalex-snapshot
Aneuploid embryos as a proposal for improving Artificial Intelligence performance
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Machine Learning in Event-Triggered Control: Recent Advances and Open Issues
inferred← citeshttps://doi.org/10.1109/access.2022.3200473openalex-snapshot
Material Classification for Terahertz Images Based on Neural Networks
inferred← citeshttps://doi.org/10.1109/access.2023.3333894openalex-snapshot
Steel Strip Quality Assurance With YOLOV7-CSF: A Coordinate Attention and SIoU Fusion Approach
inferred← citeshttps://doi.org/10.1109/access.2024.3453664openalex-snapshot
Deep Learning and Computer Vision Techniques for Enhanced Quality Control in Manufacturing Processes
inferred← citeshttps://doi.org/10.1109/access.2024.3487839openalex-snapshot
A Fuzzy-Multi Attribute Decision Making Scheme for Efficient User-Centric EV Charging Station Selection
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A Comprehensive Review on Deep Learning-Based Data Fusion
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Hybrid Machine Learning for CNC Process Monitoring
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Deep Learning for Violence Detection in Surveillance: The Role of Transfer Learning and Pre-Trained Models
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Sound Based Machine Learning for Inserts Wear Detection in Interrupted Cutting
inferred← citeshttps://doi.org/10.1109/bigdata55660.2022.10020532openalex-snapshot
A Statistical Correlation Analysis of Big Data in Machining
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Pre-training for Industrial Defect Detection
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CNN-Based Breast Cancer Ultrasound Medical Image Recognition
inferred← citeshttps://doi.org/10.1109/iccr67387.2025.11292443openalex-snapshot
Real-Time Detection of Cybercrime and Digital Forensics Automation Using AI-Based Legal Evidence Collection and Preservation Mechanisms
inferred← citeshttps://doi.org/10.1109/iceccme62383.2024.10796886openalex-snapshot
Node Classification in CAD Data: A Graph Learning Approach for Machining Feature Localization
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Intelligent production line thermal error compensation strategy system based on IoT big data
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Tool Wear Status Identification Method Based on Tdistributed Stochastic Neighbor Embedding and Gradient Boosting Decision Tree Integrated Model
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Deep Transfer Learning Based Multi-source Heterogeneous data Fusion with Application to Cross-scenario Tool Wear monitoring
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Design and Validation of a YOLOv8 Synthetic Data Generation Pipeline for Surface Defect Detection in Manufacturing
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