Scopus
YÖKSİS DOI Eşleşti
SJR Q1
A state-of-the-art review on sensors and signal processing systems in mechanical machining processes
International Journal of Advanced Manufacturing Technology · Ekim 2021
YÖKSİS Kayıtları
A state-of-the-art review on sensors and signal processing systems in mechanical machining processes
The International Journal of Advanced Manufacturing Technology · 2021 SCI-Expanded
Doç. Dr. EMİN SALUR →
A state-of-the-art review on sensors and signal processing systems in mechanical machining processes
The International Journal of Advanced Manufacturing Technology · 2021 SCI-Expanded
Doç. Dr. MUSTAFA KUNTOĞLU →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Cost optimization of submersible motors using a genetic algorithm and a finite element method
2007 ISSN: 0268-3768 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
Modelling of Dynamic Cutting Force Coefficients and Chatter Stability Dependent on Shear Angle Oscillation
2017 ISSN: 0268-3768 SCI-Expanded
Prof. Dr. SÜLEYMAN NEŞELİ →
A simple approach to analyze process damping in chatter vibration
2014 ISSN: 0268-3768 SCI-Expanded
Prof. Dr. SÜLEYMAN NEŞELİ →
Failure analysis of support during profile cutting process using horizontal milling machine
2014 ISSN: 0268-3768 SCI-Expanded 1 atıf
Prof. Dr. SÜLEYMAN NEŞELİ →
A state-of-the-art review on sensors and signal processing systems in mechanical machining processes
2021 ISSN: 0268-3768 SCI-Expanded Q2
Doç. Dr. EMİN SALUR →
Influence of tool path strategies on machining time, tool wear, and surface roughness during milling of AISI X210Cr12 steel
2022 ISSN: 0268-3768 SCI-Expanded Q2
Dr. Öğr. Üyesi ÜSAME ALİ USCA →
Investigations on tool wear, surface roughness, cutting temperature, and chip formation in machining of Cu-B-CrC composites
2021 ISSN: 0268-3768 SCI-Expanded Q2
Doç. Dr. MUSTAFA KUNTOĞLU →
Influence of tool path strategies on machining time, tool wear, and surface roughness during milling of AISI X210Cr12 steel
2022 ISSN: 0268-3768 SCI Q2
Doç. Dr. MUSTAFA KUNTOĞLU →
Indirect monitoring of machining characteristics via advanced sensor systems: a critical review
2022 ISSN: 0268-3768 SCI-Expanded Q2
Doç. Dr. MUSTAFA KUNTOĞLU →
Tool wear and machinability investigations in dry turning of Cu/Mo-SiCp hybrid composites
2021 ISSN: 0268-3768 SCI-Expanded Q2
Doç. Dr. MUSTAFA KUNTOĞLU →
A state of the art on surface morphology of selective laser-melted metallic alloys
2023 ISSN: 0268-3768 SCI-Expanded Q2
Dr. Öğr. Üyesi EYÜB CANLI →
Theoretical and experimental research of edge inclination angle effect on minimum uncut chip thickness in oblique cutting of C45 steel
2022 ISSN: 0268-3768 SCI-Expanded Q2
Doç. Dr. MUSTAFA KUNTOĞLU →
An investigation of the shearing performance and sheared surface characterisation of ultra-strength DP steel-Al explosive welded plate composite
2023 ISSN: 0268-3768 SCI-Expanded Q2
Prof. Dr. MUSTAFA ACARER →
Makale Bilgileri
ISSN02683768
Yayın TarihiEkim 2021
Cilt / Sayfa116 · 2711-2735
Scopus ID2-s2.0-85109259365
Özet
Sensors are the main equipment of the data-based enterprises for diagnosis of the health of system. Offering time- or frequency-dependent systemic information provides prognosis with the help of early-warning system using intelligent signal processing systems. Therefore, a chain of data-based information improves the efficiency especially focusing on the determination of remaining useful life of a machine or tool. A broad utilization of sensors in machining processes and artificial intelligence–supported data analysis and signal processing systems are prominent technological tools in the way of Industry 4.0. Therefore, this paper outlines the state of the art of the mentioned systems encountered in the open literature. As a result, existing studies using sensor systems including signal processing facilities in machining processes provide important contribution for error minimization and productivity maximization. However, there is a need for improved adaptive control systems for faster convergence and physical intervention in case of possible problems and failures. On the other hand, sensor fusion is an innovative new technology that makes decisions using multi-sensor information to determine tool status and predict system stability. It is currently not a fully accepted and practiced method. In a nutshell, despite their numerous advantages in terms of efficiency, time saving, and cost, the current situation of sensors used in the industry is not a sufficient level due to the investment cost and its increase with additional signal acquisition hardware and software equipment. Therefore, more studies that can contribute to the literature are needed.
Yazarlar (5)
1
Mustafa Kuntoğlu
ORCID: 0000-0002-7291-9468
2
Emin Salur
3
Munish Kumar Gupta
ORCID: 0000-0002-0777-1559
4
Murat Sarıkaya
5
Danil Yurievich Pimenov
Anahtar Kelimeler
Artificial intelligence
Industry 4.0
Machining
Sensors: signal processing
Kurumlar
Opole University of Technology
Opole Poland
Selçuk Üniversitesi
Selçuklu Turkey
Shandong University
Jinan China
Sinop Üniversitesi
Sinop Turkey
South Ural State University
Chelyabinsk Russian Federation
Scimago Dergi (ISSN Eşleşmesi)
International Journal of Advanced Manufacturing Technology
Q1
SJR Skoru0,706
H-Index175
YayıncıSpringer London
ÜlkeUnited Kingdom
Industrial and Manufacturing Engineering (Q1)
Computer Science Applications (Q2)
Control and Systems Engineering (Q2)
Mechanical Engineering (Q2)
Software (Q2)
Metrikler
114
Atıf
5
Yazar
4
Anahtar Kelime