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State-of-the-art methods and results in tool condition monitoring: a review

https://doi.org/10.1007/s00170-004-2038-2
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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FEATURE EXTRACTION BASED ON MORLET WAVELET AND ITS APPLICATION FOR MECHANICAL FAULT DIAGNOSIS
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Application of the wavelet transform to fault detection in a spur gear
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NATURAL FREQUENCIES AND DAMPINGS IDENTIFICATION USING WAVELET TRANSFORM: APPLICATION TO REAL DATA
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DECOMPOSITION OF GEAR VIBRATION SIGNALS BY THE GENERALISED S TRANSFORM
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APPLICATION OF THE ENVELOPE AND WAVELET TRANSFORM ANALYSES FOR THE DIAGNOSIS OF INCIPIENT FAULTS IN BALL BEARINGS
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A Knowledge-Based System for the Selection of Cutting Tools and Data for High Speed Machining of Moulds / Dies
inferredcites →https://doi.org/10.1007/978-1-4471-0777-4_27openalex-snapshot
Detection of Gear Failures in Helical Gears by Using Wavelet Transforms
inferredcites →https://doi.org/10.1007/978-1-4471-0777-4_65openalex-snapshot
Monitoring of Chatter in High Speed Endmilling Using Audio Signals Method
inferredcites →https://doi.org/10.1007/978-3-7091-2678-3_14openalex-snapshot
A Neural Network Architecture for Tool Wear Detection through Digital Camera Observations
inferredcites →https://doi.org/10.1007/978-3-7908-1841-3_36openalex-snapshot
Application of the FSOM to Machine Vibration Monitoring
inferredcites →https://doi.org/10.1007/s00170-003-1794-8openalex-snapshot
Modelling and experimental investigation of spindle and cutter dynamics for a high-precision machining center
inferredcites →https://doi.org/10.1007/s001700050062openalex-snapshot
Application of the Discrete Wavelet Transform to the Monitoring of Tool Failure in End Milling Using the Spindle Motor Current
inferredcites →https://doi.org/10.1007/s001700200148openalex-snapshot
Flank Wear Estimation in Face Milling Based on Radial Basis Function Neural Networks
inferredcites →https://doi.org/10.1007/s001700300020openalex-snapshot
Fuzzy Logic Based In-Process Tool-Wear Monitoring System in Face Milling Operations
inferredcites →https://doi.org/10.1016/0043-1648(83)90256-9openalex-snapshot
An investigation of tool wear and the vibration spectrum in milling
inferredcites →https://doi.org/10.1016/0043-1648(94)06539-xopenalex-snapshot
Tool wear monitoring in drilling using force signals
inferredcites →https://doi.org/10.1016/0043-1648(96)06944-xopenalex-snapshot
Tool wear monitoring in face milling using force signals
inferredcites →https://doi.org/10.1016/0278-6125(95)98889-eopenalex-snapshot
On-line monitoring of tool breakage in face milling using a self-organized neural network
inferredcites →https://doi.org/10.1016/0888-3270(92)90049-oopenalex-snapshot
A comparative study of non-parametric spectral estimators for application in machine vibration analysis
inferredcites →https://doi.org/10.1016/0890-6955(93)90092-9openalex-snapshot
Monitoring drill conditions with wavelet based encoding and neural networks
inferredcites →https://doi.org/10.1016/0890-6955(94)00060-wopenalex-snapshot
Frequency and time domain analyses of sensor signals in drilling—I. Correlation with drill wear
inferredcites →https://doi.org/10.1016/0890-6955(94)00115-zopenalex-snapshot
Force-based model for tool wear monitoring in face milling
inferredcites →https://doi.org/10.1016/0890-6955(94)90004-3openalex-snapshot
Sensing tool breakage in face milling with a neural network
inferredcites →https://doi.org/10.1016/0890-6955(95)00058-5openalex-snapshot
Tool condition monitoring in drilling using vibration signature analysis
inferredcites →https://doi.org/10.1016/0924-0136(94)01441-3openalex-snapshot
Tool wear and failure in the drilling of stainless steel
inferredcites →https://doi.org/10.1016/s0007-8506(07)60503-4openalex-snapshot
Tool Condition Monitoring (TCM) — The Status of Research and Industrial Application
inferredcites →https://doi.org/10.1016/s0007-8506(07)61011-7openalex-snapshot
Prediction of Tool Fracture in Drilling
inferredcites →https://doi.org/10.1016/s0007-8506(07)61586-8openalex-snapshot
Tool Break Detection by Monitoring Ultrasonic Vibrations
inferredcites →https://doi.org/10.1016/s0007-8506(07)62368-3openalex-snapshot
Online Failure Detection in Face Milling Using Discrete Wavelet Transform
inferredcites →https://doi.org/10.1016/s0007-8506(07)62394-4openalex-snapshot
On Multisensor Approach to Drill Wear Monitoring
inferredcites →https://doi.org/10.1016/s0007-8506(07)62890-xopenalex-snapshot
Improving Productivity and Part Quality in Milling of Titanium Based Impellers by Chatter Suppression and Force Control
inferredcites →https://doi.org/10.1016/s0007-8506(07)63192-8openalex-snapshot
Machinery Fault Diagnosis and Prognosis: Application of Advanced Signal Processing Techniques
inferredcites →https://doi.org/10.1016/s0007-8506(07)63244-2openalex-snapshot
Present Situation and Future Trends in Modelling of Machining Operations Progress Report of the CIRP Working Group ‘Modelling of Machining Operations’
inferredcites →https://doi.org/10.1016/s0031-9201(97)00047-2openalex-snapshot
Local S-spectrum analysis of 1-D and 2-D data
inferredcites →https://doi.org/10.1016/s0166-3615(96)00075-9openalex-snapshot
A review of machine vision sensors for tool condition monitoring
inferredcites →https://doi.org/10.1016/s0301-679x(02)00063-4openalex-snapshot
Application of discrete wavelet transform for detection of ball bearing race faults
inferredcites →https://doi.org/10.1016/s0890-6955(01)00156-0openalex-snapshot
Evaluation of a spindle-based force sensor for monitoring and fault diagnosis of machining operations
inferredcites →https://doi.org/10.1016/s0890-6955(02)00040-8openalex-snapshot
A summary of methods applied to tool condition monitoring in drilling
inferredcites →https://doi.org/10.1016/s0890-6955(96)00069-7openalex-snapshot
The use of cutting force and acoustic emission signals for the monitoring of tool insert geometry during rough face milling
inferredcites →https://doi.org/10.1016/s0890-6955(97)00066-7openalex-snapshot
On-line detection of the breakage of small diameter drills using current signature wavelet transform
inferredcites →https://doi.org/10.1016/s0890-6955(99)00073-5openalex-snapshot
Tool wear estimation in micro-machining.
inferredcites →https://doi.org/10.1016/s1474-6670(17)38052-7openalex-snapshot
Fault detection in machining via torque estimation
inferredcites →https://doi.org/10.1016/s1474-6670(17)46074-5openalex-snapshot
Supervision of Tool Wear and Surface Quality During End Milling Operations
inferredcites →https://doi.org/10.1023/a:1008980821787openalex-snapshot
A tool breakage detection system using an accelerometer sensor
inferredcites →https://doi.org/10.1023/a:1026583821221openalex-snapshot
Tool condition monitoring using reflectance of chip surface and neural network
inferredcites →https://doi.org/10.1080/00207540110107534openalex-snapshot
Acoustic emission analysis for tool wear monitoring in face milling
inferredcites →https://doi.org/10.1080/10940349908945680openalex-snapshot
ANALYSIS OF INITIAL CONTACT AND TOOL FRACTURE IN THE MILLING PROCESS
inferredcites →https://doi.org/10.1109/78.492555openalex-snapshot
Localization of the complex spectrum: the S transform
inferredcites →https://doi.org/10.1109/78.502340openalex-snapshot
Application of the wavelet transform to acoustic emission signals processing
inferredcites →https://doi.org/10.1109/78.668544openalex-snapshot
Wavelet-based statistical signal processing using hidden Markov models
inferredcites →https://doi.org/10.1109/annes.1995.499460openalex-snapshot
An optimization approach for the fuzzy control of turning operations
inferredcites →https://doi.org/10.1115/1.2801293openalex-snapshot
Variational Formulation for Optimal Multi-Cycle Deep Drilling of Small Holes
inferredcites →https://doi.org/10.1115/1.2830145openalex-snapshot
Adaptive Optimization of Face Milling Operations Using Neural Networks
inferredcites →https://doi.org/10.1115/1.2899033openalex-snapshot
A New Sensor for Real-Time Milling Tool Condition Monitoring
inferredcites →https://doi.org/10.1115/1.2899590openalex-snapshot
On-Line Detection of Drill Wear
inferredcites →https://doi.org/10.1115/1.2901634openalex-snapshot
Monitoring Rotating Tool Wear Using Higher-Order Spectral Features
inferredcites →https://doi.org/10.1115/1.2901801openalex-snapshot
On-Line Tool Breakage Detection in Turning: A Multi-Sensor Method
inferredcites →https://doi.org/10.1115/1.2901935openalex-snapshot
Self-Organizing Neural Network Application to Drill Wear Classification
inferredcites →https://doi.org/10.1115/1.2901957openalex-snapshot
Intelligent Classification and Measurement of Drill Wear
inferredcites →https://doi.org/10.1115/1.3187869openalex-snapshot
Computer-assisted Prediction of Drill-failure Using In-process Measurements of Thrust Force
inferredcites →https://doi.org/10.1115/1.3187881openalex-snapshot
The Detection of Tool Breakage in Milling Operations
inferredcites →https://doi.org/10.1115/1.3187884openalex-snapshot
Monitoring Drilling Wear States by a Fuzzy Pattern Recognition Technique
inferredcites →https://doi.org/10.1115/1.3188744openalex-snapshot
In-Process Detection of Tool Failure in Milling Using Cutting Force Models
inferredcites →https://doi.org/10.1115/1.3225684openalex-snapshot
In-Process Tool Fracture Detection
inferredcites →https://doi.org/10.1115/1.3256335openalex-snapshot
Signature Analysis Applied to Drilling
inferredcites →https://doi.org/10.1115/1.3439211openalex-snapshot
Sensing of Drill Wear and Prediction of Drill Life
inferredcites →https://doi.org/10.1117/12.217511openalex-snapshot
Research on intelligent monitoring technology of machining process
inferredcites →https://doi.org/10.1117/12.217527openalex-snapshot
Application of Kohonen neural network for tool condition monitoring
inferredcites →https://doi.org/10.1117/12.217533openalex-snapshot
Detection of tool breakage in turning operations by using neural networks
inferredcites →https://doi.org/10.1117/12.304810openalex-snapshot
Neural networks with fuzzy Petri nets for modeling a machining process
inferredcites →https://doi.org/10.1142/9789812810281_0008openalex-snapshot
On-line tool condition monitoring based on a neurofuzzy intelligent signal feature classification procedure
inferredcites →https://doi.org/10.1243/0954407011528554openalex-snapshot
Application of wavelet packets algorithm to diesel engines’ vibroacoustic signature extraction
inferredcites →https://doi.org/10.1520/stp14050sopenalex-snapshot
The Effect of Tool Vibration on Tool Wear in Ultrasonic Vibration Cutting

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Internet of Things in Welding Industries
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Tool Condition Monitoring in Metal Cutting
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Importance of Temperature in Metal Cutting and Its Proper Measurement/Modeling
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Feature Engineering for Machine Learning using a Software-Based Approach for Machining Operations
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Machine Learning as an Enabler for Automated Assistance Systems for the Classification of Tool Wear on Milling Tools
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Tool Wear Prediction Based on Ball-End Milling Tool Milling Area
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Towards Sustainable Machining: Synthetic Data Generation for Efficient Optical Tool Wear Monitoring via Generative Adversarial Networks
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Output Only Functional Series Time Dependent AutoRegressive Moving Average (FS-TARMA) Modelling of Tool Acceleration Signals for Wear Estimation
inferred← citeshttps://doi.org/10.1007/978-3-319-29956-3_16openalex-snapshot
Functional Series TARMA Models for Non-stationary Tool Vibration Signals Representation and Wear Estimation
inferred← citeshttps://doi.org/10.1007/978-3-319-65298-6_36openalex-snapshot
Tool Wear Condition Monitoring Based on Wavelet Packet Analysis and RBF Neural Network
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Investigation of the Methods for Tool Wear On-Line Monitoring during the Cutting Process
inferred← citeshttps://doi.org/10.1007/978-3-642-45176-8_13openalex-snapshot
Digital Image Processing in Machining
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Signal Changes
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Machine Tool Prognosis for Precision Manufacturing
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Application of Gabor Filter for Monitoring Wear of Single Point Cutting Tool
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Tool Condition Monitoring Techniques in Micromilling: A Review
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Monitoring Tool Wear with Sensors and Arduino in CNC Metal Cutting Turning Operation of Titanium Alloys (Ti6Al4V)
inferred← citeshttps://doi.org/10.1007/978-981-99-7011-7_5openalex-snapshot
Prediction of Tool Remaining Useful Life of NC Machine Tool Based on DTW Algorithm and LSTM Neural Network
inferred← citeshttps://doi.org/10.1007/978-981-99-8976-8_27openalex-snapshot
On-Line Wear Measurement for Micro Turning Round Tip Tool Based on Machine Vision
inferred← citeshttps://doi.org/10.1007/s00170-006-0588-1openalex-snapshot
Development of an online machining process monitoring system: a case study of the broaching process
inferred← citeshttps://doi.org/10.1007/s00170-007-1186-6openalex-snapshot
Tool condition monitoring in an end-milling operation based on the vibration signal collected through a microcontroller-based data acquisition system
inferred← citeshttps://doi.org/10.1007/s00170-008-1563-9openalex-snapshot
Machine fault diagnosis using a cluster-based wavelet feature extraction and probabilistic neural networks
inferred← citeshttps://doi.org/10.1007/s00170-009-2110-zopenalex-snapshot
Design of multisensor fusion-based tool condition monitoring system in end milling
inferred← citeshttps://doi.org/10.1007/s00170-009-2198-1openalex-snapshot
A new approach to the analysis of high-speed milling in the case of chipped inserts
inferred← citeshttps://doi.org/10.1007/s00170-009-2311-5openalex-snapshot
Images acquisition of a high-speed boring cutter for tool condition monitoring purposes
inferred← citeshttps://doi.org/10.1007/s00170-009-2509-6openalex-snapshot
Prognostics for drilling process with wavelet packet decomposition
inferred← citeshttps://doi.org/10.1007/s00170-010-2711-6openalex-snapshot
The development of an end-milling process depth of cut monitoring system
inferred← citeshttps://doi.org/10.1007/s00170-010-2907-9openalex-snapshot
Tool breakage detection in CNC high-speed milling based in feed-motor current signals
inferred← citeshttps://doi.org/10.1007/s00170-010-3135-zopenalex-snapshot
A microcontroller-based end milling cutter monitoring and management system
inferred← citeshttps://doi.org/10.1007/s00170-011-3561-6openalex-snapshot
Development of a feed rate adaption control system for high-speed rough and finish end-milling of hardened EN24 steel
inferred← citeshttps://doi.org/10.1007/s00170-011-3703-xopenalex-snapshot
Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling
inferred← citeshttps://doi.org/10.1007/s00170-011-3814-4openalex-snapshot
Envelope dynamic analysis: a new approach for milling process monitoring
inferred← citeshttps://doi.org/10.1007/s00170-012-4458-8openalex-snapshot
Study of high-frequency sound signals for tool wear monitoring in micromilling
inferred← citeshttps://doi.org/10.1007/s00170-012-4464-xopenalex-snapshot
Model development for tool wear effect on AE signal generation in micromilling
inferred← citeshttps://doi.org/10.1007/s00170-013-5116-5openalex-snapshot
Dynamic wireless passive strain measurement in CNC turning using surface acoustic wave sensors
inferred← citeshttps://doi.org/10.1007/s00170-014-5756-0openalex-snapshot
Energy-efficient machining systems: a critical review
inferred← citeshttps://doi.org/10.1007/s00170-014-5764-0openalex-snapshot
A method of using Hoelder exponents to monitor tool-edge wear in high-speed finish machining
inferred← citeshttps://doi.org/10.1007/s00170-014-6076-0openalex-snapshot
Development of a smart machining system using self-optimizing control
inferred← citeshttps://doi.org/10.1007/s00170-014-6296-3openalex-snapshot
Vibration-based estimation of tool major flank wear in a turning process using ARMA models
inferred← citeshttps://doi.org/10.1007/s00170-014-6323-4openalex-snapshot
Identification of error sources in a five-axis machine tool using FFT analysis
inferred← citeshttps://doi.org/10.1007/s00170-014-6560-6openalex-snapshot
Tool wear monitoring using naïve Bayes classifiers
inferred← citeshttps://doi.org/10.1007/s00170-014-6738-yopenalex-snapshot
Tool condition monitoring (TCM) using neural networks
inferred← citeshttps://doi.org/10.1007/s00170-015-7091-5openalex-snapshot
Tool life estimation based on acoustic emission monitoring in end-milling of H13 mould-steel
inferred← citeshttps://doi.org/10.1007/s00170-015-8303-8openalex-snapshot
Using spindle noise to monitor tool wear in a turning process
inferred← citeshttps://doi.org/10.1007/s00170-015-8325-2openalex-snapshot
Tool wear monitoring and replacement for tubesheet drilling
inferred← citeshttps://doi.org/10.1007/s00170-016-8556-xopenalex-snapshot
Joint optimization of lot-sizing and maintenance policy for a partially observable two-unit system
inferred← citeshttps://doi.org/10.1007/s00170-016-8848-1openalex-snapshot
Milling tool wear state recognition based on partitioning around medoids (PAM) clustering
inferred← citeshttps://doi.org/10.1007/s00170-017-0014-xopenalex-snapshot
Condition monitoring towards energy-efficient manufacturing: a review
inferred← citeshttps://doi.org/10.1007/s00170-017-0113-8openalex-snapshot
Multi-sensor process analysis and performance characterisation in CNC turning—a cyber physical system approach
inferred← citeshttps://doi.org/10.1007/s00170-017-0396-9openalex-snapshot
Sequential spindle current-based tool condition monitoring with support vector classifier for milling process
inferred← citeshttps://doi.org/10.1007/s00170-017-1251-8openalex-snapshot
An embedded multi-sensor system on the rotating dynamometer for real-time condition monitoring in milling
inferred← citeshttps://doi.org/10.1007/s00170-018-1768-5openalex-snapshot
Review of tool condition monitoring methods in milling processes
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Real-time cutting tool state recognition approach based on machining features in NC machining process of complex structural parts
inferred← citeshttps://doi.org/10.1007/s00170-018-2018-6openalex-snapshot
Tool wear condition monitoring based on continuous wavelet transform and blind source separation
inferred← citeshttps://doi.org/10.1007/s00170-018-2420-0openalex-snapshot
Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process
inferred← citeshttps://doi.org/10.1007/s00170-018-2534-4openalex-snapshot
Impact of hybrid workpieces on statistical process monitoring of machining operations
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In-process complex machining condition monitoring based on deep forest and process information fusion
inferred← citeshttps://doi.org/10.1007/s00170-019-04664-4openalex-snapshot
Tool condition monitoring in milling using a force singularity analysis approach
inferred← citeshttps://doi.org/10.1007/s00170-019-04811-xopenalex-snapshot
Influence of machining parameters on the polymer concrete milling process
inferred← citeshttps://doi.org/10.1007/s00170-020-05303-zopenalex-snapshot
Technical data-driven tool condition monitoring challenges for CNC milling: a review
inferred← citeshttps://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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Machine monitoring system: a decade in review
inferred← citeshttps://doi.org/10.1007/s00170-020-05751-7openalex-snapshot
Detection of tool breakage during milling process through acoustic emission
inferred← citeshttps://doi.org/10.1007/s00170-020-05955-xopenalex-snapshot
Research on automatic monitoring method of face milling cutter wear based on dynamic image sequence
inferred← citeshttps://doi.org/10.1007/s00170-021-07703-1openalex-snapshot
Basic tools for vibration analysis with applications to predictive maintenance of rotating machines: an overview
inferred← citeshttps://doi.org/10.1007/s00170-021-08339-xopenalex-snapshot
Experimental study of vibration signal for a prognostic system to prevent tool breakage in micro gun drilling
inferred← citeshttps://doi.org/10.1007/s00170-022-09762-4openalex-snapshot
Tool wear predicting based on weighted multi-kernel relevance vector machine and probabilistic kernel principal component analysis
inferred← citeshttps://doi.org/10.1007/s00170-022-09860-3openalex-snapshot
Tool condition monitoring based on the fractal analysis of current and cutting force signals during CFRP trimming
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/s00170-023-11079-9openalex-snapshot
Effects of nonlinear behaviour of linear ball guideway on chatter frequency of lathe machine tool
inferred← citeshttps://doi.org/10.1007/s00170-023-12237-9openalex-snapshot
Tool wear condition monitoring method based on relevance vector machine
inferred← citeshttps://doi.org/10.1007/s00170-023-12679-1openalex-snapshot
State-of-the-art review of applications of image processing techniques for tool condition monitoring on conventional machining processes
inferred← citeshttps://doi.org/10.1007/s00170-026-18020-wopenalex-snapshot
Tool wear condition recognition using multi-scale residual networks based on feature fusion
inferred← citeshttps://doi.org/10.1007/s10845-008-0195-0openalex-snapshot
Self adaptive diagnosis of tool wear with a microcontroller
inferred← citeshttps://doi.org/10.1007/s10845-009-0310-xopenalex-snapshot
Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties
inferred← citeshttps://doi.org/10.1007/s10845-010-0443-yopenalex-snapshot
Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process
inferred← citeshttps://doi.org/10.1007/s10845-011-0550-4openalex-snapshot
Basic computational tools and mechanical hardware for torque-based diagnostic of machining operations
inferred← citeshttps://doi.org/10.1007/s10845-013-0738-xopenalex-snapshot
Online incremental learning for tool condition classification using modified Fuzzy ARTMAP network
inferred← citeshttps://doi.org/10.1007/s10845-013-0774-6openalex-snapshot
Health assessment and life prediction of cutting tools based on support vector regression
inferred← citeshttps://doi.org/10.1007/s10845-015-1112-yopenalex-snapshot
A method for tool condition monitoring based on sensor fusion
inferred← citeshttps://doi.org/10.1007/s10845-016-1209-yopenalex-snapshot
A sensor fusion and support vector machine based approach for recognition of complex machining conditions
inferred← citeshttps://doi.org/10.1007/s10845-017-1348-9openalex-snapshot
Metacognitive learning approach for online tool condition monitoring
inferred← citeshttps://doi.org/10.1007/s10845-019-01488-7openalex-snapshot
Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations
inferred← citeshttps://doi.org/10.1007/s10845-020-01661-3openalex-snapshot
Improving the accuracy of machine-learning models with data from machine test repetitions
inferred← citeshttps://doi.org/10.1007/s10845-022-01923-2openalex-snapshot
Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review
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
inferred← citeshttps://doi.org/10.1007/s10845-024-02467-3openalex-snapshot
Comparative analysis of different machine vision algorithms for tool wear measurement during machining
inferred← citeshttps://doi.org/10.1007/s10845-026-02881-9openalex-snapshot
Data-driven tool wear monitoring guided by prior knowledge via degenerate manifold structural constraints
inferred← citeshttps://doi.org/10.1007/s10845-026-02938-9openalex-snapshot
AI-driven, deflection-based tool breakage prediction in PCB micro-drilling
inferred← citeshttps://doi.org/10.1007/s11431-008-0298-zopenalex-snapshot
The principle of second generation wavelet for milling cutter breakage detection
inferred← citeshttps://doi.org/10.1007/s11665-024-09883-9openalex-snapshot
Novel Approach to Monitoring the Surface Integrity of Aluminum 5052 Using Sound and Vibration Signals during Turning with Titanium-Coated Carbide Inserts
inferred← citeshttps://doi.org/10.1007/s12008-025-02223-8openalex-snapshot
A machine learning algorithm based cutting tool wear assessment using multi-sensorial data
inferred← citeshttps://doi.org/10.1007/s12541-020-00388-8openalex-snapshot
A Novel Tool (Single-Flute) Condition Monitoring Method for End Milling Process Based on Intelligent Processing of Milling Force Data by Machine Learning Algorithms
inferred← citeshttps://doi.org/10.1007/s12652-020-02875-2openalex-snapshot
Tool monitoring of end milling based on gap sensor and machine learning
inferred← citeshttps://doi.org/10.1007/s13198-016-0450-2openalex-snapshot
Tool life management of unmanned production system based on surface roughness by ANFIS
inferred← citeshttps://doi.org/10.1007/s13243-026-00161-6openalex-snapshot
Milling tool wear state classification and prediction using machine learning, homogeneous and heterogeneous ensemble learning approach
inferred← citeshttps://doi.org/10.1007/s40948-018-0086-6openalex-snapshot
Computational monitoring in real time: review of methods and applications
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Multi-view contrastive learning framework for tool wear detection with insufficient annotated data
inferred← citeshttps://doi.org/10.1016/j.apacoust.2009.07.004openalex-snapshot
Airborne sound emission as a process monitoring tool in the cut-off grinding of concrete
inferred← citeshttps://doi.org/10.1016/j.apacoust.2021.108456openalex-snapshot
Histogram as features for fault detection of multi point cutting tool – A data driven approach
inferred← citeshttps://doi.org/10.1016/j.applthermaleng.2011.08.020openalex-snapshot
Diesel engine condition monitoring using a multi-net neural network system with nonintrusive sensors
inferred← citeshttps://doi.org/10.1016/j.cie.2015.12.009openalex-snapshot
Optimal lot-sizing and maintenance policy for a partially observable production system
inferred← citeshttps://doi.org/10.1016/j.cirp.2008.03.053openalex-snapshot
Tool condition monitoring in micromilling based on hierarchical integration of signal measures
inferred← citeshttps://doi.org/10.1016/j.cirpj.2008.09.007openalex-snapshot
Application of AE and cutting force signals in tool condition monitoring in micro-milling
inferred← citeshttps://doi.org/10.1016/j.cirpj.2013.02.005openalex-snapshot
Application of digital image processing in tool condition monitoring: A review
inferred← citeshttps://doi.org/10.1016/j.cirpj.2022.04.009openalex-snapshot
Monitoring of in-process force coefficients and tool wear
inferred← citeshttps://doi.org/10.1016/j.cirpj.2025.09.014openalex-snapshot
Similarity-based anomaly detection method for turning of multi-material workpieces with varying axially constant blank diameter
inferred← citeshttps://doi.org/10.1016/j.compind.2006.02.012openalex-snapshot
Flexible software for condition monitoring, incorporating novelty detection and diagnostics
inferred← citeshttps://doi.org/10.1016/j.compind.2013.03.010openalex-snapshot
On-line tool wear measurement for ball-end milling cutter based on machine vision
inferred← citeshttps://doi.org/10.1016/j.compind.2022.103638openalex-snapshot
Interpretable deep learning approach for tool wear monitoring in high-speed milling
inferred← citeshttps://doi.org/10.1016/j.engappai.2023.107367openalex-snapshot
Improving the useful life of tools using active vibration control through data-driven approaches: A systematic literature review
inferred← citeshttps://doi.org/10.1016/j.engfailanal.2019.104348openalex-snapshot
Durability and tool wear investigation of HSSE-PM milling cutters within long-term tests
inferred← citeshttps://doi.org/10.1016/j.eswa.2013.11.005openalex-snapshot
Tool condition monitoring using K-star algorithm
inferred← citeshttps://doi.org/10.1016/j.eswa.2015.05.008openalex-snapshot
Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics
inferred← citeshttps://doi.org/10.1016/j.ifacol.2016.11.041openalex-snapshot
Estimate of Cutting Tool Lifespan through Cox Proportional Hazards Model
inferred← citeshttps://doi.org/10.1016/j.ifacol.2019.11.420openalex-snapshot
Tool Breakage Detection of Milling Cutter Insert Based on SVM
inferred← citeshttps://doi.org/10.1016/j.ijmachtools.2006.01.001openalex-snapshot
A new versatile in-process monitoring system for milling
inferred← citeshttps://doi.org/10.1016/j.ijmachtools.2006.05.008openalex-snapshot
A wavelet-based methodology for grinding wheel condition monitoring
inferred← citeshttps://doi.org/10.1016/j.ijmachtools.2007.09.001openalex-snapshot
End milling tool breakage detection using lifting scheme and Mahalanobis distance
inferred← citeshttps://doi.org/10.1016/j.ijmachtools.2009.01.012openalex-snapshot
Minor cutting edge–workpiece interactions in drilling of an advanced nickel-based superalloy
inferred← citeshttps://doi.org/10.1016/j.ijmachtools.2014.10.011openalex-snapshot
Real-time tool wear monitoring in milling using a cutting condition independent method
inferred← citeshttps://doi.org/10.1016/j.ijmachtools.2016.10.005openalex-snapshot
The concept and progress of intelligent spindles: A review
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Systematic review on tool breakage monitoring techniques in machining operations
inferred← citeshttps://doi.org/10.1016/j.ijmecsci.2019.105254openalex-snapshot
Vibration singularity analysis for milling tool condition monitoring
inferred← citeshttps://doi.org/10.1016/j.ijpe.2015.07.034openalex-snapshot
Joint optimal lot sizing and preventive maintenance policy for a production facility subject to condition monitoring
inferred← citeshttps://doi.org/10.1016/j.isatra.2012.08.003openalex-snapshot
Diagnosis of broken-bars fault in induction machines using higher order spectral analysis
inferred← citeshttps://doi.org/10.1016/j.jare.2025.01.013openalex-snapshot
Review of advanced sensor system applications in grinding operations
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Tool life predictions in milling using spindle power with the neural network technique
inferred← citeshttps://doi.org/10.1016/j.jmapro.2019.01.011openalex-snapshot
Monitoring tip-based nanomachining process by time series analysis using support vector machine
inferred← citeshttps://doi.org/10.1016/j.jmapro.2020.09.018openalex-snapshot
Intelligent technology in grinding process driven by data: A review
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Preliminary study of phase-shifting strobo-stereoscopy for cutting tool monitoring
inferred← citeshttps://doi.org/10.1016/j.jmapro.2021.06.016openalex-snapshot
Tool wear analysis of cutting Ti-5553 with uncoated carbide tool under liquid nitrogen cooling condition using tool wear maps
inferred← citeshttps://doi.org/10.1016/j.jmapro.2022.05.034openalex-snapshot
Tool damage state condition monitoring in milling processes based on the mechanistic model goodness-of-fit metrics
inferred← citeshttps://doi.org/10.1016/j.jmapro.2023.03.043openalex-snapshot
A semisupervised autoencoder-based method for anomaly detection in cutting tools
inferred← citeshttps://doi.org/10.1016/j.jmapro.2023.07.077openalex-snapshot
Tool wear process monitoring by damping behavior of cutting vibration for milling process
inferred← citeshttps://doi.org/10.1016/j.jmapro.2024.07.002openalex-snapshot
Multi-sensor signal fusion for tool wear condition monitoring using denoising transformer auto-encoder Resnet
inferred← citeshttps://doi.org/10.1016/j.jmatprotec.2008.11.038openalex-snapshot
Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion
inferred← citeshttps://doi.org/10.1016/j.jmatprotec.2017.03.016openalex-snapshot
TCM system in contour milling of very thick-very large steel plates based on vibration and AE signals
inferred← citeshttps://doi.org/10.1016/j.jmsy.2018.03.003openalex-snapshot
Holistic approach to machine tool data analytics
inferred← citeshttps://doi.org/10.1016/j.jmsy.2020.01.005openalex-snapshot
Calibration-based tool condition monitoring for repetitive machining operations
inferred← citeshttps://doi.org/10.1016/j.jmsy.2023.07.007openalex-snapshot
Multi-scale one-dimensional convolution tool wear monitoring based on multi-model fusion learning skills
inferred← citeshttps://doi.org/10.1016/j.jmsy.2024.04.001openalex-snapshot
Tool wear and remaining useful life estimation in precision machining using interacting multiple model
inferred← citeshttps://doi.org/10.1016/j.jmsy.2025.08.014openalex-snapshot
Tool wear prediction in milling process using physics-informed machine learning and thermo-mechanical force model with monitoring applications
inferred← citeshttps://doi.org/10.1016/j.jocs.2017.07.014openalex-snapshot
ENN-based recognition method for tool cutting state
inferred← citeshttps://doi.org/10.1016/j.matpr.2018.06.203openalex-snapshot
Tool condition monitoring during hard turning of AISI 52100 Steel: A case study
inferred← citeshttps://doi.org/10.1016/j.matpr.2019.05.386openalex-snapshot
Adaptive tool condition monitoring system: A brief review
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Design of bagged tree ensemble for carbide coated inserts fault diagnosis
inferred← citeshttps://doi.org/10.1016/j.matpr.2021.08.299openalex-snapshot
Importance of condition monitoring in mechanical domain
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Tool condition monitoring technique for deep-hole drilling of large components based on chatter identification in time–frequency domain
inferred← citeshttps://doi.org/10.1016/j.measurement.2022.111501openalex-snapshot
Multi-sensor heterogeneous data-based online tool health monitoring in milling of IN718 superalloy using OGM (1, N) model and SVM
inferred← citeshttps://doi.org/10.1016/j.measurement.2022.111503openalex-snapshot
Application of measurement systems in tool condition monitoring of Milling: A review of measurement science approach
inferred← citeshttps://doi.org/10.1016/j.measurement.2024.116460openalex-snapshot
Audio Images: Analyzing impingement sounds of an air jet to develop a new tool wear measurement system
inferred← citeshttps://doi.org/10.1016/j.measurement.2026.121569openalex-snapshot
An adaptive recognition model for tool wear state across multiple datasets
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Condition monitoring for pattern recognition in manufacturing
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Formulation and Comparison of Experimental based Mathematical Model with Artificial Neural Network Simulation and RSM (Response Surface Methodology) Model for Optimal Performance of Sliver Cutting Operation of Bamboo
inferred← citeshttps://doi.org/10.1016/j.mspro.2014.07.462openalex-snapshot
Fault Diagnosis of Single Point Cutting Tool through Vibration Signal Using Decision Tree Algorithm
inferred← citeshttps://doi.org/10.1016/j.neucom.2012.11.013openalex-snapshot
Data driven modeling based on dynamic parsimonious fuzzy neural network
inferred← citeshttps://doi.org/10.1016/j.precisioneng.2010.05.001openalex-snapshot
A long-term control scheme of cutting forces to regulate tool life in end milling processes
inferred← citeshttps://doi.org/10.1016/j.precisioneng.2015.11.001openalex-snapshot
Detection of fracture in ceramic cutting tools from workpiece profile signature using image processing and fast Fourier transform
inferred← citeshttps://doi.org/10.1016/j.precisioneng.2016.01.003openalex-snapshot
Machine vision micro-milling tool wear inspection by image reconstruction and light reflectance
inferred← citeshttps://doi.org/10.1016/j.precisioneng.2016.09.014openalex-snapshot
Detection of chipping in ceramic cutting inserts from workpiece profile during turning using fast Fourier transform (FFT) and continuous wavelet transform (CWT)
inferred← citeshttps://doi.org/10.1016/j.procir.2020.03.034openalex-snapshot
Process monitoring with a cyber-physical cutting tool
inferred← citeshttps://doi.org/10.1016/j.procir.2023.02.113openalex-snapshot
Integrating Carbon Credits with Life Cycle Costing for Economic Sustainability in SMEs
inferred← citeshttps://doi.org/10.1016/j.procir.2023.03.075openalex-snapshot
An investigation of the effect of tool wear on cutting force coefficients for solid end mills
inferred← citeshttps://doi.org/10.1016/j.procir.2023.09.252openalex-snapshot
Sensor-based identification of tool wear in turning
inferred← citeshttps://doi.org/10.1016/j.procir.2024.08.377openalex-snapshot
Influence of illumination on the image-based classification accuracy of wear on milling tools
inferred← citeshttps://doi.org/10.1016/j.procs.2015.04.047openalex-snapshot
Machine Learning Approach to the Prediction of Surface Roughness Using Statistical Features of Vibration Signal Acquired in Turning
inferred← citeshttps://doi.org/10.1016/j.procs.2024.01.158openalex-snapshot
Tool Condition Monitoring for milling process using Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1016/j.promfg.2018.06.024openalex-snapshot
Tools 4.0 – Intelligence starts on the cutting edge
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Integrated Tool Condition Monitoring Systems and Their Applications: A Comprehensive Review
inferred← citeshttps://doi.org/10.1016/j.promfg.2021.06.008openalex-snapshot
Modeling in-process machining data using spatial point cloud vs. time series data structures
inferred← citeshttps://doi.org/10.1016/j.rcim.2016.11.008openalex-snapshot
Tool condition monitoring in interrupted cutting with acceleration sensors
inferred← citeshttps://doi.org/10.1016/j.rcim.2016.12.009openalex-snapshot
Fuzzy logic based tool condition monitoring for end-milling
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Prognosticating RULs while exploiting the future characteristics of operating profiles
inferred← citeshttps://doi.org/10.1016/j.sna.2013.09.024openalex-snapshot
Torque measurements of an automotive halfshaft using micro double-ended tuning fork strain gauges
inferred← citeshttps://doi.org/10.1016/j.sna.2015.11.017openalex-snapshot
Design and development of a cutting force sensor based on semi-conductive strain gauge
inferred← citeshttps://doi.org/10.1016/j.sna.2019.07.026openalex-snapshot
Contactless monitoring of temperature change in cutting inserts by application of hard coatings and ferromagnetic film sensor phases
inferred← citeshttps://doi.org/10.1016/j.wear.2011.01.038openalex-snapshot
Monitoring online cutting tool wear using low-cost technique and user-friendly GUI
inferred← citeshttps://doi.org/10.1016/j.wear.2014.11.012openalex-snapshot
Sliding wear evaluation of aluminum alloy (7075-T6) on hardened steel (AISI4340) via non-contact technique by I-kaz™ multi-level analysis
inferred← citeshttps://doi.org/10.1016/j.wear.2025.206397openalex-snapshot
Determining and predicting the flank wear width and residual stress of coated tools by Raman imaging
inferred← citeshttps://doi.org/10.1016/j.ymssp.2007.06.005openalex-snapshot
Grinding wheel condition monitoring with boosted minimum distance classifiers
inferred← citeshttps://doi.org/10.1016/j.ymssp.2012.05.001openalex-snapshot
Applying the self-organization feature map (SOM) algorithm to AE-based tool wear monitoring in micro-cutting
inferred← citeshttps://doi.org/10.1016/j.ymssp.2013.02.004openalex-snapshot
Angular approach combined to mechanical model for tool breakage detection by eddy current sensors
inferred← citeshttps://doi.org/10.1016/j.ymssp.2016.11.026openalex-snapshot
Neural network approach for automatic image analysis of cutting edge wear
inferred← citeshttps://doi.org/10.1016/j.ymssp.2017.11.022openalex-snapshot
Predicting tool life in turning operations using neural networks and image processing
inferred← citeshttps://doi.org/10.1016/j.ymssp.2021.107738openalex-snapshot
Sound singularity analysis for milling tool condition monitoring towards sustainable manufacturing
inferred← citeshttps://doi.org/10.1016/j.ymssp.2022.109434openalex-snapshot
Online damage detection of cutting tools using Dirichlet process mixture models
inferred← citeshttps://doi.org/10.1016/j.ymssp.2022.109851openalex-snapshot
Tool wear monitoring based on multi-kernel Gaussian process regression and Stacked Multilayer Denoising AutoEncoders
inferred← citeshttps://doi.org/10.1016/j.ymssp.2023.110925openalex-snapshot
Multiresolution analysis for tool failure detection in CFRP/Ti6Al4V hybrid stacks drilling in aircraft assembly lines
inferred← citeshttps://doi.org/10.1016/j.ymssp.2024.111949openalex-snapshot
Application of physics-guided deep learning model in tool wear monitoring of high-speed milling
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