Advanced machining of inconel 690 using alumina-enriched sunflower oil-based lubricant: a genetic algorithm-driven approach

被引:0
作者
Bhowmik, Abhijit [1 ,2 ]
Kumar, Raman [3 ,4 ]
Jhala, Ramdevsinh [5 ]
Beemkumar, N. [6 ]
Kumar, Ambati Vijay [7 ]
Kedia, Ankit [8 ]
Kumar, K. Durga Hemanth [9 ]
Singh, Gurpartap [10 ]
机构
[1] Dream Inst Technol, Dept Mech Engn, Kolkata 700104, India
[2] Chitkara Univ, Chitkara Univ Inst Engn & Technol, Ctr Res Impact & Outcome, Rajpura 140401, Punjab, India
[3] Rayat Bahra Univ, Univ Sch Mech Engn, Kharar 140103, Punjab, India
[4] Sohar Univ, Fac Engn, POB 44, Sohar PCI 311, Oman
[5] Marwadi Univ, Marwadi Univ Res Ctr, Fac Engn & Technol, Dept Mech Engn, Rajkot 360003, Gujarat, India
[6] JAIN, Sch Engn & Technol, Dept Mech Engn, Bangalore, Karnataka, India
[7] Raghu Engn Coll, Dept Mech Engn, Visakhapatnam 531162, Andhra Pradesh, India
[8] NIMS Univ Rajasthan, NIMS Inst Engn & Technol, NIMS Sch Mech & Aerosp Engn, Jaipur, India
[9] SRKR Engn Coll, Dept Mech Engn, Bhimavaram 534204, India
[10] Chandigarh Engn Coll, Chandigarh Grp Coll Jhanjeri, Dept Mech Engn, Mohali 140307, Punjab, India
来源
INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM | 2025年
关键词
Hard machining; Surface morphology; Tool wear mechanism; Surface protection film; Optimization; SURFACE-ROUGHNESS; VEGETABLE-OIL; TOOL WEAR; ALLOY; OPTIMIZATION; PARAMETERS; ENERGY; MOS2; DRY;
D O I
10.1007/s12008-025-02246-1
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This study explores the potential of using vegetable oil enhanced with nanoparticles as a metalworking fluid. Various alumina concentrations (0-1%) were mixed into sunflower oil, and thermo-physical properties analysis was used to identify the optimal concentration of alumina nanoparticles. Subsequently, hard milling of Inconel 690 was performed in diverse lubrication conditions: dry, compressed air, sunflower oil, and sunflower oil with alumina. The comparative analysis revealed that sunflower oil with 0.8% alumina significantly outperformed the other conditions, reducing surface roughness, cutting force, and cutting temperature by 42.28%, 27.40%, and 23.44%, respectively, compared to dry cutting. Finally, twenty-seven Taguchi-based experiments were conducted under the best lubrication conditions, and a Genetic Algorithm (GA) was employed to optimize the machining environment. Practical experiments confirmed the optimized conditions, showing that the mean error between experimental and predicted results was 0.37%. The novel combination of eco-friendly lubrication and AI-driven optimization addresses critical challenges in machining efficiency and sustainability, offering a framework for reducing energy consumption, minimizing environmental impact, and improving tool longevity. The findings highlight a transformative approach to sustainable manufacturing, advancing the application of nano-green lubricants in machining superalloys.
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页数:12
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