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An Intelligent Milling Tool Wear Monitoring Methodology Based on Convolutional Neural Network with Derived Wavelet Frames Coefficient

https://doi.org/10.3390/app9183912
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s00170-012-4177-1openalex-snapshot
A review of flank wear prediction methods for tool condition monitoring in a turning process
inferredcites →https://doi.org/10.1007/s00521-005-0467-yopenalex-snapshot
Revisiting squared-error and cross-entropy functions for training neural network classifiers
inferredcites →https://doi.org/10.1007/s10845-013-0738-xopenalex-snapshot
Online incremental learning for tool condition classification using modified Fuzzy ARTMAP network
inferredcites →https://doi.org/10.1007/s10845-016-1209-yopenalex-snapshot
A sensor fusion and support vector machine based approach for recognition of complex machining conditions
inferredcites →https://doi.org/10.1007/s10845-017-1302-xopenalex-snapshot
Automatic feature constructing from vibration signals for machining state monitoring
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A novel integrated tool condition monitoring system
inferredcites →https://doi.org/10.1007/s11740-018-0866-4openalex-snapshot
Tool flank wear monitoring using torsional–axial vibrations in drilling
inferredcites →https://doi.org/10.1007/s12541-016-0103-zopenalex-snapshot
Tool-wear monitoring during micro-end milling using wavelet packet transform and Fisher’s linear discriminant
inferredcites →https://doi.org/10.1007/s13198-017-0637-1openalex-snapshot
Face milling tool condition monitoring using sound signal
inferredcites →https://doi.org/10.1016/j.advengsoft.2014.02.002openalex-snapshot
Force based tool wear monitoring system for milling process based on relevance vector machine
inferredcites →https://doi.org/10.1016/j.aei.2017.02.005openalex-snapshot
Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification
inferredcites →https://doi.org/10.1016/j.cirpj.2014.04.005openalex-snapshot
Principal component analysis for feature extraction and NN pattern recognition in sensor monitoring of chip form during turning
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
inferredcites →https://doi.org/10.1016/j.engappai.2017.07.024openalex-snapshot
Machining vibration states monitoring based on image representation using convolutional neural networks
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Approach into the use of probabilistic neural networks for automated classification of tool malfunctions in broaching
inferredcites →https://doi.org/10.1016/j.ijmachtools.2014.10.011openalex-snapshot
Real-time tool wear monitoring in milling using a cutting condition independent method
inferredcites →https://doi.org/10.1016/j.isatra.2018.05.009openalex-snapshot
Sparsity-based signal extraction using dual Q-factors for gearbox fault detection
inferredcites →https://doi.org/10.1016/j.jmapro.2016.06.015openalex-snapshot
State of health monitoring in machining: Extended Kalman filter for tool wear assessment in turning of IN718 hard-to-machine alloy
inferredcites →https://doi.org/10.1016/j.jsv.2017.03.044openalex-snapshot
Weighted low-rank sparse model via nuclear norm minimization for bearing fault detection
inferredcites →https://doi.org/10.1016/j.matpr.2017.11.521openalex-snapshot
Online Monitoring of Tool Wear and Surface Roughness by using Acoustic and Force Sensors
inferredcites →https://doi.org/10.1016/j.neucom.2016.07.009openalex-snapshot
Traffic sign detection and recognition using fully convolutional network guided proposals
inferredcites →https://doi.org/10.1016/j.patcog.2016.04.007openalex-snapshot
DropSample : A new training method to enhance deep convolutional neural networks for large-scale unconstrained handwritten Chinese character recognition
inferredcites →https://doi.org/10.1016/j.sigpro.2013.04.015openalex-snapshot
Wavelets for fault diagnosis of rotary machines: A review with applications
inferredcites →https://doi.org/10.1016/j.sna.2013.09.023openalex-snapshot
Hybrid learning based Gaussian ARTMAP network for tool condition monitoring using selected force harmonic features
inferredcites →https://doi.org/10.1016/j.ymssp.2009.06.015openalex-snapshot
Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform
inferredcites →https://doi.org/10.1016/j.ymssp.2011.10.018openalex-snapshot
CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks
inferredcites →https://doi.org/10.1016/j.ymssp.2013.10.007openalex-snapshot
The relationship between kurtosis- and envelope-based indexes for the diagnostic of rolling element bearings
inferredcites →https://doi.org/10.1016/j.ymssp.2014.03.011openalex-snapshot
Selection of informative frequency band in local damage detection in rotating machinery
inferredcites →https://doi.org/10.1016/j.ymssp.2015.11.027openalex-snapshot
Sparsity-based algorithm for detecting faults in rotating machines
inferredcites →https://doi.org/10.1016/j.ymssp.2016.06.035openalex-snapshot
Repetitive transients extraction algorithm for detecting bearing faults
inferredcites →https://doi.org/10.1016/j.ymssp.2017.06.022openalex-snapshot
A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load
inferredcites →https://doi.org/10.1016/s0007-8506(07)60200-5openalex-snapshot
Advancing Cutting Technology
inferredcites →https://doi.org/10.1016/s0007-8506(07)60877-4openalex-snapshot
Dry Cutting
inferredcites →https://doi.org/10.1023/a:1010091220143openalex-snapshot
The Cross-Entropy Method for Combinatorial and Continuous Optimization
inferredcites →https://doi.org/10.1049/cje.2016.01.023openalex-snapshot
DropConnect Regularization Method with Sparsity Constraint for Neural Networks
inferredcites →https://doi.org/10.1080/00207540500536913openalex-snapshot
Sensor fusion for online tool condition monitoring in milling
inferredcites →https://doi.org/10.1088/1361-6501/aa6e22openalex-snapshot
An adaptive deep convolutional neural network for rolling bearing fault diagnosis
inferredcites →https://doi.org/10.1109/mim.2016.7384960openalex-snapshot
Improving calibration accuracy of a vibration sensor through a closed loop measurement system
inferredcites →https://doi.org/10.1109/msp.2005.1550194openalex-snapshot
The dual-tree complex wavelet transform
inferredcites →https://doi.org/10.1109/tie.2013.2274422openalex-snapshot
Multimodal Hidden Markov Model-Based Approach for Tool Wear Monitoring
inferredcites →https://doi.org/10.1109/tim.2013.2281576openalex-snapshot
Tool Condition Monitoring of Single-Point Dresser Using Acoustic Emission and Neural Networks Models
inferredcites →https://doi.org/10.1115/1.1511177openalex-snapshot
Analysis of Sound Signal Generation Due to Flank Wear in Turning
inferredcites →https://doi.org/10.1115/1.4031770openalex-snapshot
Tool Condition Monitoring in Turning by Applying Machine Vision
inferredcites →https://doi.org/10.1115/1.538886openalex-snapshot
Flank Wear Estimation in Turning Through Wavelet Representation of Acoustic Emission Signals
inferredcites →https://doi.org/10.1142/s1469026818500177openalex-snapshot
Acoustic Emission-Based Tool Condition Classification in a Precision High-Speed Machining of Titanium Alloy: A Machine Learning Approach
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1515/bpasts-2016-0071openalex-snapshot
Developing automatic recognition system of drill wear in standard laminated chipboard drilling process
inferredcites →https://doi.org/10.20944/preprints201703.0092.v1openalex-snapshot
Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation
inferredcites →https://doi.org/10.21437/interspeech.2013-438openalex-snapshot
Estimating phoneme class conditional probabilities from raw speech signal using convolutional neural networks
inferredcites →https://doi.org/10.23919/icact.2018.8323641openalex-snapshot
Detection of rail surface defects based on CNN image recognition and classification
inferredcites →https://doi.org/10.3115/v1/d14-1181openalex-snapshot
Convolutional Neural Networks for Sentence Classification
inferredcites →https://doi.org/10.3139/120.111318openalex-snapshot
Tool condition monitoring in the milling process with vegetable based cutting fluids using vibration signatures
inferredcites →https://doi.org/10.3390/ma10070790openalex-snapshot
An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network
inferredcites →https://doi.org/10.3390/s17122885openalex-snapshot
Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation
inferredcites →https://doi.org/10.3901/jme.2014.17.077openalex-snapshot
Novel Ensemble Analytic Discrete Framelet Expansion for Machinery Fault Diagnosis
inferredcites →https://doi.org/10.48550/arxiv.1212.5701openalex-snapshot
ADADELTA: An Adaptive Learning Rate Method

What points at this work

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

inferred← citeshttps://doi.org/10.2139/ssrn.4674643openalex-extracted
Cnn-Gru Model for Uhpc Fracture Mode Classification Using Wavelet Time-Frequency Spectrum Derived from Acoustic Emission Waveform
inferred← citeshttps://doi.org/10.1007/978-3-031-49011-8_39openalex-snapshot
Tool Wear Monitoring Using Multi-sensor Time Series and Machine Learning
inferred← citeshttps://doi.org/10.1007/s00170-020-05890-xopenalex-snapshot
Tool wear state recognition based on gradient boosting decision tree and hybrid classification RBM
inferred← citeshttps://doi.org/10.1007/s00170-020-05931-5openalex-snapshot
Tool condition monitoring in milling process using multifractal detrended fluctuation analysis and support vector machine
inferred← citeshttps://doi.org/10.1007/s00170-021-07319-5openalex-snapshot
The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation
inferred← citeshttps://doi.org/10.1007/s00170-021-07325-7openalex-snapshot
A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges
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Tool wear condition monitoring method based on relevance vector machine
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Tool condition monitoring of diamond-coated burrs with acoustic emission utilising machine learning methods
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Tool Wear Monitoring of a Tree Log Bandsaw using a Deep Convolutional Neural Network on challenging data
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