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Tool wear state recognition based on GWO–SVM with feature selection of genetic algorithm

https://doi.org/10.1007/s00170-019-03906-9
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What this work says

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

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Support-Vector Networks
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A new predictive model based on the PSO-optimized support vector machine approach for predicting the milling tool wear from milling runs experimental data
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Tool wear monitoring based on kernel principal component analysis and v-support vector regression
inferredcites →https://doi.org/10.1007/s00170-017-0367-1openalex-snapshot
Force-based tool wear estimation for milling process using Gaussian mixture hidden Markov models
inferredcites →https://doi.org/10.1007/s00170-017-1391-xopenalex-snapshot
An integrated wireless vibration sensing tool holder for milling tool condition monitoring
inferredcites →https://doi.org/10.1007/s00170-017-1460-1openalex-snapshot
Application of audible sound signals for tool wear monitoring using machine learning techniques in end milling
inferredcites →https://doi.org/10.1007/s00170-018-2420-0openalex-snapshot
Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process
inferredcites →https://doi.org/10.1007/s10845-015-1112-yopenalex-snapshot
A method for tool condition monitoring based on sensor fusion
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Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks
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Grey Wolf Optimizer
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.apm.2013.10.073openalex-snapshot
Modeling of EDM responses by support vector machine regression with parameters selected by particle swarm optimization
inferredcites →https://doi.org/10.1016/j.apsusc.2018.08.117openalex-snapshot
Hole-like surface morphologies on the stainless steel surface through laser surface texturing underwater
inferredcites →https://doi.org/10.1016/j.compeleceng.2013.11.024openalex-snapshot
A survey on feature selection methods
inferredcites →https://doi.org/10.1016/j.engappai.2009.09.004openalex-snapshot
Feature extraction and selection from acoustic emission signals with an application in grinding wheel condition monitoring
inferredcites →https://doi.org/10.1016/j.ijleo.2018.05.096openalex-snapshot
Color difference classification based on optimization support vector machine of improved grey wolf algorithm
inferredcites →https://doi.org/10.1016/j.jmapro.2017.11.014openalex-snapshot
In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm
inferredcites →https://doi.org/10.1016/j.measurement.2014.08.035openalex-snapshot
Monitoring and processing signal applied in machining processes – A review
inferredcites →https://doi.org/10.1016/j.neucom.2015.04.069openalex-snapshot
Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization
inferredcites →https://doi.org/10.1016/j.sna.2014.01.004openalex-snapshot
Vibration sensor based tool condition monitoring using ν support vector machine and locality preserving projection
inferredcites →https://doi.org/10.1016/j.wear.2018.05.012openalex-snapshot
Tool wear monitoring in rock drilling applications using vibration signals
inferredcites →https://doi.org/10.1023/b:stco.0000035301.49549.88openalex-snapshot
A tutorial on support vector regression
inferredcites →https://doi.org/10.1109/cecnet.2011.5768795openalex-snapshot
Research on GA-SVM tool wear monitoring method using HHT characteristics of drilling noise signals
inferredcites →https://doi.org/10.1109/icdma.2011.67openalex-snapshot
Aviation Tool Wear States Identifying Based on EMD and SVM
inferredcites →https://doi.org/10.1109/tii.2017.2723943openalex-snapshot
Online Tool Wear Monitoring Via Hidden Semi-Markov Model With Dependent Durations
inferredcites →https://doi.org/10.1109/wcica.2010.5554471openalex-snapshot
Cutting tool wear identification based on wavelet package and SVM
inferredcites →https://doi.org/10.1115/1.4040267openalex-snapshot
Robust Tool Wear Monitoring Using Systematic Feature Selection in Turning Processes With Consideration of Uncertainties
inferredcites →https://doi.org/10.1145/1167253.1167288openalex-snapshot
Decision tree classifier for network intrusion detection with GA-based feature selection
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/10.17559/tvopenalex-snapshot
Tehnicki vjesnik - Technical Gazette
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Face Milling Tool Wear Condition Monitoring Based on Wavelet Transform and Shannon Entropy
inferredcites →https://doi.org/10.4028/www.scientific.net/msf.797.17openalex-snapshot
A Tool Wear Monitoring System for Steel and Aluminium Alloys Based on the same Sensor Signals and Decision Strategy

What points at this work

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

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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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Unsupervised Deep Transfer Learning Model for Tool Wear States Recognition
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In-process tap tool wear monitoring and prediction using a novel model based on deep learning
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Tool Wear State Recognition in CNC Milling using Reliable Labels, Robust Domain Features, and Lightweight AI Models
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A Novel Rolling Bearing Fault Diagnosis Method Based on E-RTH Multi-parameter Optimization of LSSVM
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