Scopus
🔓 Açık Erişim YÖKSİS DOI Eşleşti
SJR Q1
A review of indirect tool condition monitoring systems and decision‐making methods in turning: Critical analysis and trends
Sensors Switzerland · Ocak 2021
YÖKSİS Kayıtları
A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends.
Sensors (Basel, Switzerland) · 2021 SCI-Expanded
Dr. Öğr. Üyesi ÜSAME ALİ USCA →
A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends
Sensors · 2020 SCI-Expanded
Doç. Dr. MUSTAFA KUNTOĞLU →
A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends
Sensors · 2020 SCI-Expanded
Doç. Dr. EMİN SALUR →
YÖKSİS Kayıtları — ISSN Eşleşmesi
A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends.
2021 ISSN: 1424-8220 SCI-Expanded Q2
Dr. Öğr. Üyesi ÜSAME ALİ USCA →
A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends
2020 ISSN: 1424-8220 SCI-Expanded Q1
Doç. Dr. MUSTAFA KUNTOĞLU →
A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends
2020 ISSN: 1424-8220 SCI-Expanded Q1
Doç. Dr. EMİN SALUR →
Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140
2020 ISSN: 1424-8220 SCI-Expanded
Doç. Dr. MUSTAFA KUNTOĞLU →
Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types
2023 ISSN: 1424-8220 SCI-Expanded Q2
Doç. Dr. YAVUZ SELİM TAŞPINAR →
Survey on Blockchain-Based Data Storage Security for Android Mobile Applications
2023 ISSN: 1424-8220 SCI-Expanded Q2
Prof. Dr. ADEM ALPASLAN ALTUN →
Optimizing Autonomous Vehicle Performance Using Improved Proximal Policy Optimization
2025 ISSN: 1424-8220 SCI-Expanded
Dr. Öğr. Üyesi ONUR İNAN →
Comparative Analysis of Machine Learning Methods with Chaotic AdaBoost and Logistic Mapping for Real-Time Sensor Fusion in Autonomous Vehicles: Enhancing Speed and Acceleration Prediction Under Uncertainty
2025 ISSN: 1424-8220 SCI-Expanded
Dr. Öğr. Üyesi ONUR İNAN →
Makale Bilgileri
Dergi
Sensors Switzerland
ISSN14248220
Yayın TarihiOcak 2021
Cilt / Sayfa21 · 1-33
Scopus ID2-s2.0-85098792933
Erişim🔓 Açık Erişim
Özet
The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In‐direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emis-sion, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without in-tervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration re-quire a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.
Yazarlar (10)
1
Mustafa Kuntoğlu
ORCID: 0000-0002-7291-9468
2
Abdullah Aslan
3
Danil Yurievich Pimenov
4
Üsame Ali Usca
5
Emin Salur
6
Munish Kumar Gupta
ORCID: 0000-0002-0777-1559
7
Tadeusz Mikolajczyk
ORCID: 0000-0002-5253-590X
8
Khaled Giasin
9
Wojciech Kapłonek
10
Shubham Sharma
Anahtar Kelimeler
Acoustic emission
Current
Cutting force
Indirect tool condition monitoring systems
Industry 4.0
Machining
Temperature
Turning
Vibration
Kurumlar
Bingöl Üniversitesi
Bingol Turkey
Bydgoszcz University of Science and Technology
Bydgoszcz Poland
Politechnika Koszalinska
Koszalin Poland
Punjab Technical University
Jalandhar India
Selçuk Üniversitesi
Selçuklu Turkey
Shandong University
Jinan China
South Ural State University
Chelyabinsk Russian Federation
University of Portsmouth
Portsmouth United Kingdom
Scimago Dergi (ISSN Eşleşmesi)
Sensors
Q1
OA
SJR Skoru0,764
H-Index273
YayıncıMultidisciplinary Digital Publishing Institute (MDPI)
ÜlkeSwitzerland
Analytical Chemistry (Q1)
Instrumentation (Q1)
Atomic and Molecular Physics, and Optics (Q2)
Biochemistry (Q2)
Electrical and Electronic Engineering (Q2)
Information Systems (Q2)
Medicine (miscellaneous) (Q2)
Metrikler
255
Atıf
10
Yazar
9
Anahtar Kelime