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Scopus YÖKSİS DOI Eşleşti SJR Q1

Characterizing Machining Indicators with Machine Learning Models Under Cellulose Nanocrystal and Graphene-Based Nanofluid Conditions

Arabian Journal for Science and Engineering · Şubat 2026

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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
Doç. Dr. MUSTAFA KUNTOĞLU →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 15 kaydı bulundu.

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A New Lifetime Model: Properties, Estimation, and Applications in Quality Control
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Brain Tumor Detection with Transfer Learning Models Based on Attention Modules
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Effect of Silica/Graphene Nanohybrid Particles on the Mechanical Properties of Epoxy Coatings
2019 ISSN: 2193-567X SCI-Expanded Q3
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Heuristic Optimization Based on Penalty Approach for Surface Permanent Magnet Synchronous Machines
2020 ISSN: 2193-567X SCI-Expanded Q1
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A Method of Classification Performance Improvement Via a Strategy of Clustering-Based Data Elimination Integrated with k-Fold Cross-Validation
2021 ISSN: 2193-567X SCI-Expanded Q2
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Training Feed-Forward Multi-Layer Perceptron Artificial Neural Networks with a Tree-Seed Algorithm
2020 ISSN: 2193-567X SCI-Expanded Q3
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Solving Multi‑Objective Resource Allocation Problem Using Multi‑Objective Binary Artificial Bee Colony Algorithm
2021 ISSN: 2193-567X SCI-Expanded Q3
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Theoretical and Experimental Investigation of the Performance of an Atkinson Cycle Engine
2021 ISSN: 2193-567X SCI-Expanded Q3
Dr. Öğr. Üyesi HALİL ERDİ GÜLCAN →
Optimization of Electricity Generation Parameters with Microbial Fuel Cell Using the Response Surface Method
2022 ISSN: 2193-567X SCI-Expanded Q2
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The Innovative Approach to Real-Time Detection of Fuel Types Based on Ultrasonic Sensor and Machine Learning
2024 ISSN: 2193-567X SCI-Expanded Q2
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Enhanced Gain Dual-Port Compact Printed Meandered Log-Periodic Monopole Array Antenna Design with Octagonal-Ring Shaped FSS for Broadband 28 GHz Applications
2024 ISSN: 2193-567X SCI-Expanded Q2
Dr. Öğr. Üyesi MEHMET YERLİKAYA →
The Innovative Approach to Real-Time Detection of Fuel Types Based on Ultrasonic Sensor and Machine Learning
2024 ISSN: 2193-567X SCI-Expanded Q2
Dr. Öğr. Üyesi UĞUR TAŞKIRAN →
Determination of Temperature Effects on Cortical Bone Milling Using Taguchi Method
2025 ISSN: 2193-567X SCI-Expanded Q2
Prof. Dr. SÜLEYMAN NEŞELİ →
Characterizing Machining Indicators with Machine Learning Models Under Cellulose Nanocrystal and Graphene-Based Nanofluid Conditions
2025 ISSN: 2193-567X SCI-Expanded Q2
Doç. Dr. MUSTAFA KUNTOĞLU →

Makale Bilgileri

Dergi Arabian Journal for Science and Engineering
ISSN2193567X
Yayın TarihiŞubat 2026
Cilt / Sayfa51 · 3089-3105
DOI10.1007/s13369-025-10240-8
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
Scimago Dergi (ISSN Eşleşmesi)
Arabian Journal for Science and Engineering
Q1
SJR Skoru0,545
H-Index89
ÜlkeGermany
Multidisciplinary (Q1)
Dergi sayfasına git

Metrikler

3
Atıf
3
Yazar
5
Anahtar Kelime

Hızlı Erişim

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