Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 发表日期：

    2025年2月10日

查看全文
  • Abhijit Bhowmik,
  • Raman Kumar,
  • Ramdevsinh Jhala,
  • N. Beemkumar,
  • Ambati Vijay Kumar,
  • Ankit Kedia,
  • K. Durga Hemanth Kumar,
  • Gurpartap Singh

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.