Next-generation MQL strategies for optimizing process performance in AlSi1MgMn machining using a hybrid decision-making framework
International Journal of Advanced Manufacturing Technology, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s00170-026-18767-2
- Dergi Adı: International Journal of Advanced Manufacturing Technology
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, Compendex, INSPEC, DIALNET, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: Cutting performance, EN AW 6082 aluminum, Hybrid nanofluid-MQL, Multi-objective optimization, Sustainable machining
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
The transition toward cleaner, more energy-efficient manufacturing has intensified the need for advanced lubrication strategies that reduce the environmental and operational burdens of machining processes. High-speed machining of aluminum alloys faces persistent challenges, including excessive heat generation, high energy consumption, and limited effectiveness of conventional lubrication strategies. Although minimum quantity lubrication (MQL) reduces environmental impact compared with flood cooling, its performance at elevated cutting speeds remains inadequate, and existing nanofluid-based enhancements are often material-specific and insufficiently optimized. This study proposes an integrated experimental-optimization framework to evaluate next-generation nanofluid-assisted MQL strategies. Fe₃O₄ nanofluid, graphene nanofluid, and a hybrid graphene + Fe₃O₄ nanofluid were prepared and compared with dry cutting, air-blowing, and conventional MQL during high-speed turning of EN AW 6082. Cutting force, cutting temperature, energy consumption, and material removal rate were experimentally measured and analyzed using Response Surface Regression, followed by multi-objective optimization with NSGA-II and final solution selection using GRA, MARCOS, and VIKOR methods. Hybrid nanofluid-assisted MQL achieved up to 25% reduction in cutting force, 30% reduction in cutting temperature, and 40% reduction in energy consumption compared with dry and conventional MQL, while maintaining high material removal rates. Enhanced wettability and tribofilm formation further supported its superior performance. The proposed framework can be extended to other alloys and machining processes to support cleaner and more energy-efficient manufacturing.