Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review
Journal of Intelligent Manufacturing · Haziran 2023
YÖKSİS Kayıtları
Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review
Journal of Intelligent Manufacturing · 2023 SCI-Expanded
Doç. Dr. MUSTAFA KUNTOĞLU →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Intelligent design of induction motors by multiobjective fuzzy genetic algorithm
2010 ISSN: 0956-5515 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
Parallel inference in dynamic decision support systems of a semiotic type
2004 ISSN: 0956-5515 SCI-Expanded
Doç. Dr. KEMAL TÜTÜNCÜ →
Determination of the drug dose by fuzzy expert system in treatment of chronic intestine inflammation
2009 ISSN: 0956-5515 SCI-Expanded 18 atıf Q1
Prof. Dr. İSMAİL SARITAŞ →
Determination of the drug dose by fuzzy expert system in treatment of chronic intestine inflammation
2009 ISSN: 0956-5515 SCI-Expanded
Doç. Dr. İLKER ALİ ÖZKAN →
Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review
2023 ISSN: 0956-5515 SCI-Expanded Q1
Doç. Dr. MUSTAFA KUNTOĞLU →
Makale Bilgileri
ISSN09565515
Yayın TarihiHaziran 2023
Cilt / Sayfa34 · 2079-2121
Scopus ID2-s2.0-85126103275
Özet
The wear of cutting tools, cutting force determination, surface roughness variations and other machining responses are of keen interest to latest researchers. The variations of these machining responses results in change in dimensional accuracy and productivity upto great extent. In addition, an excessive increase in wear leads to catastrophic consequences, exceeding the tool breakage. Therefore, this article discusses the online trend of modern approaches in tool condition monitoring while different machining operations. For this purpose, the effective use of new sensors and artificial intelligence (AI) is considered and followed during this holistic review work. The sensor systems used for monitoring tool wear are dynamometers, accelerometers, acoustic emission sensors, current and power sensors, image sensors, other sensors. These systems allow to solve the problem of automation and modeling of technological parameters of the main types of cutting, such as turning, milling, drilling and grinding. The modern artificial intelligence methods are considered, such as: Neural networks, Image recognition, Fuzzy logic, Adaptive neuro-fuzzy inference systems, Bayesian Networks, Support vector machine, Ensembles, Decision and regression trees, k-nearest neighbors, Artificial Neural Network, Markov model, Singular Spectrum Analysis, Genetic algorithms. Discussions also includes the main advantages, disadvantages and prospects of using various AI methods for tool wear monitoring. Moreover, the problems and future directions of the main processing methods using AI models are also highlighted.
Yazarlar (6)
1
Danil Yurievich Pimenov
2
Andres Bustillo
ORCID: 0000-0003-2855-7532
3
Szymon Wojciechowski
ORCID: 0000-0002-3380-4588
4
Vishal S. Sharma
ORCID: 0000-0002-6200-7422
5
Munish Kumar Gupta
ORCID: 0000-0002-0777-1559
6
Mustafa Kuntoğlu
ORCID: 0000-0002-7291-9468
Anahtar Kelimeler
Artificial intelligence
Machining
Sensor
Tool condition monitoring
Tool life
Wear
Kurumlar
Opole University of Technology
Opole Poland
Politechnika Poznanska
Poznan Poland
Selçuk Üniversitesi
Selçuklu Turkey
South Ural State University
Chelyabinsk Russian Federation
Universidad de Burgos
Burgos Spain
University of the Witwatersrand, Johannesburg
Johannesburg South Africa
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Scimago Dergi (ISSN Eşleşmesi)
Journal of Intelligent Manufacturing
Q1
SJR Skoru1,888
H-Index124
YayıncıSpringer Netherlands
ÜlkeNetherlands
Artificial Intelligence (Q1)
Industrial and Manufacturing Engineering (Q1)
Software (Q1)
Metrikler
308
Atıf
6
Yazar
6
Anahtar Kelime