Scopus
YÖKSİS Eşleşti
Characterizing Machining Indicators with Machine Learning Models Under Cellulose Nanocrystal and Graphene-Based Nanofluid Conditions
Arabian Journal for Science and Engineering · Ocak 2025
YÖKSİS Kayıtları
Characterizing Machining Indicators with Machine Learning Models Under Cellulose Nanocrystal and Graphene-Based Nanofluid Conditions
Arabian Journal for Science and Engineering · 2025 SCI-Expanded
DOÇENT MUSTAFA KUNTOĞLU →
Makale Bilgileri
DergiArabian Journal for Science and Engineering
Yayın TarihiOcak 2025
Scopus ID2-s2.0-105005225920
Özet
With outstanding physical properties such as superior ductility and strength, ultra-high strength steels (UHSS) have recently been broadly preferred as industrial materials. In this context, this study investigates the machinability of UHSS S1100 material under different cooling/lubricating conditions. The efficacy of environmentally friendly cooling/lubricating techniques, namely dry, MQL and nanofluid cellulose nanocrystal and graphene nanoplatelets-based MQL, was investigated with different cutting parameters. This novel study evaluated the influence of machining conditions and parameters on responses such as tool wear, surface roughness, energy consumption, cutting temperatures and chip morphology while incorporating machine learning. In addition, correlation analysis was performed with machine learning and the relationships between input and output parameters were evaluated. Lubricating methods such as pure MQL, cellulose nanocrystal and graphene nanoplatelets-based nanofluid are pivotal in heat transfer management and decrease cutting temperatures, tool wear and energy consumption. NGPN-based nanofluid and pure MQL environments at low feed rates and high cutting speeds resulted in the best surface quality. This work provides important insights into the machinability improvement of UHSS S1100 material implementing nanofluids and machine learning models.
Yazarlar (3)
1
Mustafa Kuntoğlu
ORCID: 0000-0002-7291-9468
2
Rüstem Binali
ORCID: 0000-0003-0775-3817
3
Mayur A. Makhesana
Anahtar Kelimeler
Cellulose nanocrystal
Graphene nanoplatelets
Machine learning
Nano-MQL
UHSS
Kurumlar
Nirma University, Institute of Technology
Ahmedabad India
Selçuk Üniversitesi
Selçuklu Turkey