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<p>This study investigates the application of high-speed milling techniques to optimize machining processes on INCONEL 718 using TiN-coated carbide tools. The study evaluates how changes in cutting speed, feed rate, and concentration of Al<sub>2</sub>O<sub>3</sub> powder affect surface roughness and tool wear. The study attempts to determine the ideal process parameters using Nano-MQL (minimum quantity lubricant) and nanopowder Al<sub>2</sub>O<sub>3</sub> in a mixture of palm oil and different concentrations. A central composite design (CCD) was utilized in a Design of Experiments (DoE) methodology to assess various parameter combinations. ANOVA analysis was created with mathematical models for flank wear, tool nose wear, and surface roughness Ra and Rt. Genetic algorithms were applied to refine the optimization process further, guided by response surface methodology. The results, presented through graphs generated from the Design of Experiments, were subsequently analyzed using scanning electron microscopy (SEM) to evaluate surface properties with greater precision.</p>

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High-speed machining of INCONEL 718: enhancing surface roughness and tool life using TiN-coated carbide tools

  • 期刊论文

  • 发表日期：

    2025年5月8日

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  • A.K.M. Nurul Amin,
  • Sinthea Khatun,
  • Tasfia Saba,
  • Mashiat Iqbal,
  • Md Jalal Uddin Rumi

摘要

This study investigates the application of high-speed milling techniques to optimize machining processes on INCONEL 718 using TiN-coated carbide tools. The study evaluates how changes in cutting speed, feed rate, and concentration of Al2O3 powder affect surface roughness and tool wear. The study attempts to determine the ideal process parameters using Nano-MQL (minimum quantity lubricant) and nanopowder Al2O3 in a mixture of palm oil and different concentrations. A central composite design (CCD) was utilized in a Design of Experiments (DoE) methodology to assess various parameter combinations. ANOVA analysis was created with mathematical models for flank wear, tool nose wear, and surface roughness Ra and Rt. Genetic algorithms were applied to refine the optimization process further, guided by response surface methodology. The results, presented through graphs generated from the Design of Experiments, were subsequently analyzed using scanning electron microscopy (SEM) to evaluate surface properties with greater precision.