Scopus
YÖKSİS DOI Eşleşti
SJR Q2
Sustainable Lubrication Strategies in Eco-friendly Machining of AISI 4140 Steel: Performance and Environmental Impact Analysis Using Machine Learning
Journal of Materials Engineering and Performance · Şubat 2026
YÖKSİS Kayıtları
Sustainable Lubrication Strategies in Eco-friendly Machining of AISI 4140 Steel: Performance and Environmental Impact Analysis Using Machine Learning
Journal of Materials Engineering and Performance · 2025 SCI-Expanded
Doç. Dr. MUSTAFA KUNTOĞLU →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Effect of Graphene Nanoplatelets on Progressive Failure Behavior under Internal Pressure of Composite Cylindrical Pressure Vessels
2022 ISSN: 1059-9495 SCI-Expanded Q4
Doç. Dr. HARUN SEPETÇİOĞLU →
Evaluation of Machinability of Cu Matrix Composite Materials by Computer Numerical Control Milling under Cryogenic LN2 and Minimum Quantity Lubrication
2023 ISSN: 1059-9495 SCI-Expanded Q3
Dr. Öğr. Üyesi ÜSAME ALİ USCA →
Effect of Impact Angle and Speed, and Weight Abrasive Concentration on AISI 1015 and 304 Steel Exposed to Erosive Wear
2024 ISSN: 1059-9495 SCI-Expanded Q3
Prof. Dr. REFİK POLAT →
Effect of Addition of CNTxGnPyhBNz Ternary Hybrid Nanofillers on Mechanical Performance of Al Nanocomposites: A Comparative Study
2025 ISSN: 1059-9495 SCI Q3
Prof. Dr. RECAİ KUŞ →
Sustainable Lubrication Strategies in Eco-friendly Machining of AISI 4140 Steel: Performance and Environmental Impact Analysis Using Machine Learning
2025 ISSN: 1059-9495 SCI-Expanded Q3
Doç. Dr. MUSTAFA KUNTOĞLU →
Built-up-edge Formation and its Effect on Surface Topography and Machinability Indicators in Sustainable Minimum Quantity Lubrication Turning of Al2024-T6
2025 ISSN: 1059-9495 SCI-Expanded Q3
Doç. Dr. MUSTAFA KUNTOĞLU →
Life Cycle and Machinability Analysis of Milling Incoloy 286 Using Sustainable Cooling/Lubrication Technologies
2025 ISSN: 1059-9495 SCI-Expanded Q3
Doç. Dr. MUSTAFA KUNTOĞLU →
Makale Bilgileri
ISSN10599495
Yayın TarihiŞubat 2026
Cilt / Sayfa35 · 4962-4978
Scopus ID2-s2.0-105015083465
Özet
This study encompasses extensive analysis for different aspects of industrially important 4140 steel during dry and minimum quantity lubrication-assisted turning operations. Surface roughness, tool wear, cutting forces, chip morphology and cutting temperatures were considered as technological parameters while carbon emissions and energy consumption were handled as the ecological parameters. The environmental analysis indicates that increased cutting speeds and greater depths of cut result in a substantial rise in energy consumption, with levels reaching up to 50% higher than those seen in alternative configurations. In the case of high cutting speeds, carbon emissions can potentially increase by as much as 60%. Conversely, at low cutting speeds and parameters, energy consumption emissions decrease by 42%. In terms of carbon emissions, dry machining offers a distinct advantage over MQL. Machine learning (decision tree model) is utilized to model the effects of input and output parameters to determine the optimum values of these parameters. It has provided the relationship between the dependent variables and the independent variables for sustainable machining of an industrially important material. The decision tree ML model for cutting force results showed that RMSE values are 8.7 and 11.89 for dry and MQL environments, while it was 6.83 and 1.15 for cutting temperature in dry and MQL environments, respectively. Finally, RMSE values of surface roughness are 0.19 and 0.16 for dry and MQL environments, respectively.
Yazarlar (6)
1
Mustafa Kuntoğlu
ORCID: 0000-0002-7291-9468
2
Havva Demirpolat
ORCID: 0000-0002-2981-9867
3
Rüstem Binali
ORCID: 0000-0003-0775-3817
4
Mehmet Erdi Korkmaz
5
Mayur A. Makhesana
6
Kübra Kaya
ORCID: 0000-0002-9971-8826
Anahtar Kelimeler
environmental analysis
lubrication
machine learning-assisted manufacturing
sustainable machining
Kurumlar
Karabük Üniversitesi
Karabuk Turkey
Nirma University, Institute of Technology
Ahmedabad India
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Journal of Materials Engineering and Performance
Q2
SJR Skoru0,487
H-Index98
YayıncıSpringer New York
ÜlkeUnited States
Materials Science (miscellaneous) (Q2)
Mechanical Engineering (Q2)
Mechanics of Materials (Q2)
Metrikler
1
Atıf
6
Yazar
4
Anahtar Kelime