4.6 Review

A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges

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。
Article Engineering, Manufacturing

Online Chatter Detection for Milling Operations Using LSTM Neural Networks Assisted by Motor Current Signals of Ball Screw Drives

Rajiv Kumar Vashisht et al.

Summary: This study proposes an online chatter detection method based on current signals applied to the ball screw drive of CNC machine to detect self-excited vibrations of milling tools. The method eliminates the need for additional sensors and utilizes neural networks to achieve a high level of accuracy in experiments.

JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME (2021)

Article Engineering, Multidisciplinary

Tool wear mechanism and prediction in milling TC18 titanium alloy using deep learning

Junyan Ma et al.

Summary: The study introduced a real-life tool wear experiment for milling TC18, and analyzed the three stages of tool wear in detail based on micro-topography and chemical elements. By utilizing deep learning methods, tool wear prediction models were established, offering a new approach for monitoring tool wear on-line with errors within 8% for predicted minimum values.

MEASUREMENT (2021)

Article Computer Science, Artificial Intelligence

Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time

Serkan Ayvaz et al.

Summary: The study developed a data-driven predictive maintenance system for manufacturing production lines, utilizing real-time data from IoT sensors to detect potential failures before they occur. The system was successful in identifying indicators of potential failures and the best performing machine learning models were integrated into the production system in the factory.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Article Automation & Control Systems

In-process tap tool wear monitoring and prediction using a novel model based on deep learning

Xingwei Xu et al.

Summary: A novel model based on deep learning was proposed to monitor and predict tap tool wear, which showed superior robustness and accuracy in tool wear prediction compared to state-of-the-art algorithms. The model was trained using vibration signals and tool wear data collected during a tapping experiment from a real engine cylinder head production line.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)

Article Automation & Control Systems

Prediction of surface roughness based on a hybrid feature selection method and long short-term memory network in grinding

Weicheng Guo et al.

Summary: This article presents a novel prediction system for surface roughness by collecting signals during grinding process, extracting features, and utilizing long short-term memory network for accurate prediction. The proposed system shows excellent prediction performance and reduced costs, proving its practicality and feasibility.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)

Article Automation & Control Systems

Cutting tool temperature monitoring in circular sawing: measurement and multi-sensor feature fusion-based prediction

Vahid Nasir et al.

Summary: This study utilized contact thermocouple temperature measurements and sensor fusion to monitor the tool temperature during wood circular sawing, revealing significant impacts of cutting parameters on tool temperature, with rotation speed having the most complex and nonlinear effect on blade temperature. Among the investigated sensors, AE sensor showed better performance for tool temperature monitoring and performed best when combined with vibration signals.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)

Article Automation & Control Systems

Prediction of the remaining useful life of cutting tool using the Hurst exponent and CNN-LSTM

Xiaoyang Zhang et al.

Summary: A novel systematic methodology integrating signal partition and deep learning strategies was designed to improve the accuracy and computational efficiency of predicting the remaining useful life (RUL) of cutting tools. Comprehensive comparisons with mainstream algorithms showed that the proposed methodology achieved significantly better prediction accuracy.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)

Article Engineering, Multidisciplinary

Intelligent analysis of tool wear state using stacked denoising autoencoder with online sequential-extreme learning machine

Jiayu Ou et al.

Summary: A new method using SDAE and OS-ELM for intelligent recognition of tool wear states was proposed in this research. By collecting current signals of the spindle of CNC machine tool and utilizing a new neural network model, effective tool wear recognition was achieved.

MEASUREMENT (2021)

Article Computer Science, Interdisciplinary Applications

Tool Wear Online Monitoring Method Based on DT and SSAE-PHMM

Xiangyu Zhang et al.

Summary: This paper proposed an online tool wear monitoring method based on digital twin and artificial intelligence, which established a real-time tool state reflecting digital twin model and used Stack Sparse Auto-Encoder-parallel hidden Markov model for tool wear state recognition, realizing accurate monitoring and recognition of tool wear states.

JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING (2021)

Article Engineering, Multidisciplinary

Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism

Xingwei Xu et al.

Summary: Tool wear is a key factor in the cutting process, and accurate prediction using deep learning methods can improve the prediction accuracy.

MEASUREMENT (2021)

Article Engineering, Multidisciplinary

An edge-labeling graph neural network method for tool wear condition monitoring using wear image with small samples

Gaofeng Zhi et al.

Summary: This paper introduces a new TCM method based on EGNN for small training datasets, using CNN to extract features and a fully connected graph to predict tool wear condition, demonstrating superior performance in milling TCM experiments.

MEASUREMENT SCIENCE AND TECHNOLOGY (2021)

Article Chemistry, Analytical

Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process

Nika Brili et al.

Summary: This article presents a control system for monitoring cutting tool condition during turning, using an infrared camera and convolutional neural network for tool wear and damage prediction. The classification accuracy of the system was 99.55%, allowing for immediate action in case of tool wear or breakage.

SENSORS (2021)

Article Automation & Control Systems

Heterogeneous sensors-based feature optimisation and deep learning for tool wear prediction

Xiaoyang Zhang et al.

Summary: This paper presents a systematic methodology that combines signal de-noising, feature extraction, optimization, and deep learning prediction for tool wear monitoring. Experimental results show that this method can significantly improve data quality, reduce the number of extracted features effectively, and achieve high prediction accuracy.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)

Article Engineering, Multidisciplinary

Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling

Mohsen Marani et al.

Summary: This paper introduces a prediction model for tool flank wear during the machining of a steel alloy using LSTM modeling. The results demonstrate the accuracy and reliability of the model, with a low test RMSE value.

MEASUREMENT (2021)

Article Engineering, Mechanical

A chatter detection method in milling of thin-walled TC4 alloy workpiece based on auto-encoding and hybrid clustering

Yichao Dun et al.

Summary: In this study, a new unsupervised method is proposed to diagnose chatter stability in milling based on unlabeled dynamic signals, which is not susceptible to measurement errors, does not require labels, and demonstrates robustness.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2021)

Article Engineering, Mechanical

Research on tool wear prediction based on temperature signals and deep learning

Zhaopeng He et al.

Summary: Tool condition monitoring is crucial for efficient tool life utilization, production improvement, and cost reduction. A deep learning method using temperature signals was proposed to predict tool wear accurately. The experimental results showed that the proposed model outperformed traditional methods in terms of prediction accuracy and stability.
Article Computer Science, Artificial Intelligence

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)

Article Computer Science, Artificial Intelligence

Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery

Biao Wang et al.

NEUROCOMPUTING (2020)

Article Automation & Control Systems

An unsupervised online monitoring method for tool wear using a sparse auto-encoder

Jianming Dou et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)

Article Automation & Control Systems

An optimized convolutional neural network for chatter detection in the milling of thin-walled parts

Weiguo Zhu et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)

Article Automation & Control Systems

Modeling and analysis of tool wear prediction based on SVD and BiLSTM

Xiaoqiang Wu et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)

Article Automation & Control Systems

Deep neural network-based cost function for metal cutting data assimilation

Takashi Misaka et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)

Article Automation & Control Systems

Milling chatter detection using scalogram and deep convolutional neural network

Tran Minh-Quang et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)

Article Computer Science, Artificial Intelligence

A hybrid information model based on long short-term memory network for tool condition monitoring

Weili Cai et al.

JOURNAL OF INTELLIGENT MANUFACTURING (2020)

Article Engineering, Multidisciplinary

A novel transformer-based neural network model for tool wear estimation

Hui Liu et al.

MEASUREMENT SCIENCE AND TECHNOLOGY (2020)

Article Engineering, Industrial

Transfer learning for enhanced machine fault diagnosis in manufacturing

Peng Wang et al.

CIRP ANNALS-MANUFACTURING TECHNOLOGY (2020)

Article Automation & Control Systems

Intelligent wood machining monitoring using vibration signals combined with self-organizing maps for automatic feature selection

Vahid Nasir et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)

Article Computer Science, Artificial Intelligence

Machine learning and data analytics for the IoT

Erwin Adi et al.

NEURAL COMPUTING & APPLICATIONS (2020)

Article Automation & Control Systems

Tool Wear Prediction via Multidimensional Stacked Sparse Autoencoders With Feature Fusion

Chengming Shi et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

Article Automation & Control Systems

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 Automation & Control Systems

Review of tool condition monitoring in machining and opportunities for deep learning

G. Serin et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)

Article Automation & Control Systems

Intelligent recognition of milling cutter wear state with cutting parameter independence based on deep learning of spindle current clutter signal

Kaiyu Song et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)

Article Automation & Control Systems

Characterization, optimization, and acoustic emission monitoring of airborne dust emission during wood sawing

Vahid Nasir et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)

Article Computer Science, Interdisciplinary Applications

Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems

Cristina Morariu et al.

COMPUTERS IN INDUSTRY (2020)

Article Engineering, Multidisciplinary

Evaluation of turned and milled surfaces roughness using convolutional neural network

Achmad P. Rifai et al.

MEASUREMENT (2020)

Article Chemistry, Multidisciplinary

A Qualitative Tool Condition Monitoring Framework Using Convolution Neural Network and Transfer Learning

Harshavardhan Mamledesai et al.

APPLIED SCIENCES-BASEL (2020)

Article Engineering, Manufacturing

Sensor fusion and random forest modeling for identifying frozen and green wood during lumber manufacturing

Vahid Nasir et al.

MANUFACTURING LETTERS (2020)

Article Automation & Control Systems

Dimension reduction and 2D-visualization for early change of state detection in a machining process with a variational autoencoder approach

Antoine Proteau et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)

Article Green & Sustainable Science & Technology

Effect of pine impregnation and feed speed on sound level and cutting power in wood sawing

Roksana Licow et al.

JOURNAL OF CLEANER PRODUCTION (2020)

Article Multidisciplinary Sciences

CNN based tool monitoring system to predict life of cutting tool

P. K. Ambadekar et al.

SN APPLIED SCIENCES (2020)

Article Computer Science, Information Systems

An Intelligent System for Grinding Wheel Condition Monitoring Based on Machining Sound and Deep Learning

Cheng-Hsiung Lee et al.

IEEE ACCESS (2020)

Review Materials Science, Paper & Wood

A review on wood machining: characterization, optimization, and monitoring of the sawing process

Vahid Nasir et al.

WOOD MATERIAL SCIENCE & ENGINEERING (2020)

Article Automation & Control Systems

Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox

Guoqian Jiang et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)

Article Automation & Control Systems

Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprec's on Manufacturing

Chengming Shi et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)

Review Automation & Control Systems

Fundamentals of smart manufacturing: A multi-thread perspective

Andrew Kusiak

ANNUAL REVIEWS IN CONTROL (2019)

Article Automation & Control Systems

Acoustic emission monitoring of sawing process: artificial intelligence approach for optimal sensory feature selection

Vahid Nasir et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2019)

Article Computer Science, Interdisciplinary Applications

Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification

Xin-Cheng Cao et al.

COMPUTERS IN INDUSTRY (2019)

Article Forestry

Optimal power consumption and surface quality in the circular sawing process of Douglas-fir wood

Vahid Nasir et al.

EUROPEAN JOURNAL OF WOOD AND WOOD PRODUCTS (2019)

Article Automation & Control Systems

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)

Review Computer Science, Artificial Intelligence

A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures

Yong Yu et al.

NEURAL COMPUTATION (2019)

Article Automation & Control Systems

Tool wear classification using time series imaging and deep learning

Giovanna Martinez-Arellano et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2019)

Review Automation & Control Systems

A comprehensive review on minimum quantity lubrication (MQL) in machining processes using nano-cutting fluids

Zafar Said et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2019)

Article Automation & Control Systems

The use of support vector machine, neural network, and regression analysis to predict and optimize surface roughness and cutting forces in milling

Ali Yeganefar et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2019)

Article Automation & Control Systems

Tool remaining useful life prediction method based on LSTM under variable working conditions

Jing-Tao Zhou et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2019)

Article Engineering, Mechanical

Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme

Wennian Yu et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)

Article Computer Science, Interdisciplinary Applications

Deep heterogeneous GRU model for predictive analytics in smart manufacturing: Application to tool wear prediction

Jinjiang Wang et al.

COMPUTERS IN INDUSTRY (2019)

Article Computer Science, Artificial Intelligence

Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0

Alberto Diez-Olivan et al.

INFORMATION FUSION (2019)

Article Engineering, Mechanical

Deep learning and its applications to machine health monitoring

Rui Zhao et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)

Proceedings Paper Automation & Control Systems

New Approach based on Autoencoders to Monitor the Tool Wear Condition in HSM

Luis Enrique Escajeda Ochoa et al.

IFAC PAPERSONLINE (2019)

Article Engineering, Mechanical

DISTINGUISHING SENSOR FAULTS FROM SYSTEM FAULTS BY UTILIZING MINIMUM SENSOR REDUNDANCY

Morteza Taiebat et al.

TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING (2018)

Review Computer Science, Interdisciplinary Applications

Deep learning for healthcare applications based on physiological signals: A review

Oliver Faust et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2018)

Review Automation & Control Systems

Influences of tool structure, tool material and tool wear on machined surface integrity during turning and milling of titanium and nickel alloys: a review

Bing Wang et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2018)

Article Computer Science, Interdisciplinary Applications

Cloud-based manufacturing process monitoring for smart diagnosis services

Alessandra Caggiano

INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING (2018)

Article Green & Sustainable Science & Technology

An approach to cleaner production for machining hardened steel using different cooling-lubrication conditions

Mozammel Mia et al.

JOURNAL OF CLEANER PRODUCTION (2018)

Article Green & Sustainable Science & Technology

Towards sustainability assessment of machining processes

H. A. Hegab et al.

JOURNAL OF CLEANER PRODUCTION (2018)

Article Engineering, Industrial

Deep learning for smart manufacturing: Methods and applications

Jinjiang Wang et al.

JOURNAL OF MANUFACTURING SYSTEMS (2018)

Article Engineering, Industrial

Data-driven smart manufacturing

Fei Tao et al.

JOURNAL OF MANUFACTURING SYSTEMS (2018)

Review Engineering, Mechanical

A review on the application of deep learning in system health management

Samir Khan et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)

Article Computer Science, Artificial Intelligence

An overview on Restricted Boltzmann Machines

Nan Zhang et al.

NEUROCOMPUTING (2018)

Article Automation & Control Systems

Predicting tool wear with multi-sensor data using deep belief networks

Yuxuan Chen et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2018)

Review Green & Sustainable Science & Technology

Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry

Dong-Hyeon Kim et al.

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY (2018)

Article Engineering, Manufacturing

Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning

Max Ferguson et al.

SMART AND SUSTAINABLE MANUFACTURING SYSTEMS (2018)

Review Biochemical Research Methods

Deep learning for healthcare: review, opportunities and challenges

Riccardo Miotto et al.

BRIEFINGS IN BIOINFORMATICS (2018)

Article Computer Science, Cybernetics

The Internet of Things in Manufacturing Key Issues and Potential Applications

Chen Yang et al.

IEEE SYSTEMS MAN AND CYBERNETICS MAGAZINE (2018)

Article Automation & Control Systems

A novel simulation method for interaction of machining process and machine tool structure

Wanqun Chen et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2017)

Review Automation & Control Systems

State of The Art-Intense Review on Artificial Intelligence Systems Application in Process Planning and Manufacturing

S. P. Leo Kumar

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2017)

Review Automation & Control Systems

Condition monitoring towards energy-efficient manufacturing: a review

Zude Zhou et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2017)

Editorial Material Multidisciplinary Sciences

Smart manufacturing must embrace big data

Andrew Kusiak

NATURE (2017)

Article Green & Sustainable Science & Technology

Minimum Quantity Lubrication and Carbon Footprint: A Step towards Sustainability

Muhammad Omair et al.

SUSTAINABILITY (2017)

Article Multidisciplinary Sciences

Tool-Wear Analysis Using Image Processing of the Tool Flank

Ovidiu Gheorghe Moldovan et al.

SYMMETRY-BASEL (2017)

Review Computer Science, Information Systems

Applications of artificial intelligence in intelligent manufacturing: a review

Bo-hu Li et al.

FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING (2017)

Proceedings Paper Computer Science, Information Systems

A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis

Toyosi Toriola Ademujimi et al.

ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: THE PATH TO INTELLIGENT, COLLABORATIVE AND SUSTAINABLE MANUFACTURING (2017)

Article Automation & Control Systems

Tool wear predictability estimation in milling based on multi-sensorial data

P. Stavropoulos et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2016)

Article Engineering, Industrial

A novel data transformation model for small data-set learning

Der-Chiang Li et al.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2016)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Article Engineering, Multidisciplinary

Minimization of Surface Roughness and Tool Vibration in CNC Milling Operation

Sukhdev S. Bhogal et al.

JOURNAL OF OPTIMIZATION (2015)

Proceedings Paper Engineering, Manufacturing

Analysis of Feature Extracting Ability for Cutting State Monitoring Using Deep Belief Networks

Yang Fu et al.

15TH CIRP CONFERENCE ON MODELLING OF MACHINING OPERATIONS (15TH CMMO) (2015)

Article Computer Science, Hardware & Architecture

A survey on feature selection methods

Girish Chandrashekar et al.

COMPUTERS & ELECTRICAL ENGINEERING (2014)

Article Engineering, Industrial

Monitoring the tool wear, surface roughness and chip formation occurrences using multiple sensors in turning

M. S. H. Bhuiyan et al.

JOURNAL OF MANUFACTURING SYSTEMS (2014)

Article Engineering, Multidisciplinary

Monitoring and processing signal applied in machining processes - A review

C. H. Lauro et al.

MEASUREMENT (2014)

Review Computer Science, Artificial Intelligence

Representation Learning: A Review and New Perspectives

Yoshua Bengio et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2013)

Article Automation & Control Systems

CHMM for tool condition monitoring and remaining useful life prediction

Mei Wang et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2012)

Review Engineering, Manufacturing

Chatter in machining processes: A review

Guillem Quintana et al.

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE (2011)

Article Engineering, Industrial

Advanced monitoring of machining operations

R. Teti et al.

CIRP ANNALS-MANUFACTURING TECHNOLOGY (2010)

Review Automation & Control Systems

A review of machining monitoring systems based on artificial intelligence process models

Jose Vicente Abellan-Nebot et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2010)

Article Engineering, Manufacturing

Quality and Inspection of Machining Operations: Tool Condition Monitoring

John T. Roth et al.

JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME (2010)

Article Engineering, Industrial

Interaction of manufacturing process and machine tool

C. Brecher et al.

CIRP ANNALS-MANUFACTURING TECHNOLOGY (2009)

Review Engineering, Manufacturing

Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results

Zhu Kunpeng et al.

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE (2009)

Article Acoustics

An Approach for the Construction of Entropy Measure and Energy Map in Machine Fault Diagnosis

R. Tafreshi et al.

JOURNAL OF VIBRATION AND ACOUSTICS-TRANSACTIONS OF THE ASME (2009)

Review Engineering, Mechanical

Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography

ZK Peng et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2004)