Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Review of advanced sensor system applications in grinding operations
Journal of Advanced Research · Ocak 2025
YÖKSİS Kayıtları
Review of advanced sensor system applications in grinding operations
Journal of Advanced Research · 2025 SCI-Expanded
DOÇENT MUSTAFA KUNTOĞLU →
Makale Bilgileri
DergiJournal of Advanced Research
Yayın TarihiOcak 2025
Scopus ID2-s2.0-85215845375
Erişim🔓 Açık Erişim
Özet
Background: Today, in a wide variety of industries, grinding operations are an extremely important finishing process for obtaining precise dimensions and meeting strict requirements for roughness and shape accuracy. However, the constant wear of abrasive tools during grinding negatively affects the dimensional and surface conditions of the workpiece. Therefore, effective monitoring of the wear process during grinding operations helps to predict tool life, plan maintenance and ensure consistent product quality. Aim of Review: The objective of this review is to examine current tool condition monitoring techniques, both direct and indirect, in various sensor systems and their application in both traditional and AI-driven grinding processes. By examining these techniques, the review provides insight into how different monitoring techniques can improve process efficiency, reduce downtime, and improve finished product quality, as well as the application of intelligent and adaptive processes to traditional grinding operations. Key Scientific Concepts of Review: The review discusses the critical role of sensor systems in monitoring tool condition, including technologies such as imaging, vibration analysis, acoustic emission, and force measurement. These systems are vital for detecting wear and predicting failures, allowing for timely interventions and preventing unplanned downtimes. The integration of artificial intelligence into these monitoring systems greatly enhances their capabilities, as they enable more proactive strategies and adapt to changing conditions during the grinding process.
Yazarlar (6)
1
Danil Yurievich Pimenov
2
Leonardo Rosa Ribeiro da Silva
ORCID: 0000-0003-2777-4500
3
Mustafa Kuntoğlu
ORCID: 0000-0002-7291-9468
4
Bruno Souza Abrão
ORCID: 0000-0002-7251-5957
5
Luiz Eduardo dos Santos Paes
ORCID: 0000-0003-1897-9942
6
Emanoil Linul
ORCID: 0000-0001-9090-8917
Anahtar Kelimeler
Conventional and artificial intelligence applications
Grinding
Machining
Sensor systems
Tool condition monitoring
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
South Ural State University
Chelyabinsk Russian Federation
Universidade Federal de Uberlândia
Uberlandia Brazil
Universitatea Politehnica Timisoara
Timisoara Romania