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<p>Tool vibration and acoustic studies in the face milling of AISI 1045 steels are essential for ensuring high-quality surface finishes, prolonging tool life, maintaining machining stability, operational continuity, and optimizing manufacturing efficiency. These studies contribute to better process control, cost savings, and the overall reliability of the machining operations. In this work spindle speed, feed rate, and depth of cut were considered as machining parameters and tool vibration, and acoustics were considered as responses. Three levels for the parameters were selected, and using Minitab 18 software, 27 trials were generated and experiments were carried out to get the responses. Using Abaqus a numerical model was developed to find the modes of natural frequencies. Then Fast Fourier Transforms to convert the vibration and acoustic characteristics from the time domain to the frequency domain to determine the amplitude closest to the natural frequency of tool vibration and acoustic emission. For the multi-objective optimization, Response Surface Methodology was used to get the optimal combination of machining parameters. Confirmation experiments were carried out at the optimal combinations and the model is well validated<i>.</i></p>

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Multi-objective optimization of machining parameters in face milling of AISI 1045 steel to ensure operational continuity

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月2日

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  • C. S. Sumesh,
  • M Venkata Roshan,
  • Sajith S,
  • Shravan R. N,
  • Dinu Thomas Thekkuden

摘要

Tool vibration and acoustic studies in the face milling of AISI 1045 steels are essential for ensuring high-quality surface finishes, prolonging tool life, maintaining machining stability, operational continuity, and optimizing manufacturing efficiency. These studies contribute to better process control, cost savings, and the overall reliability of the machining operations. In this work spindle speed, feed rate, and depth of cut were considered as machining parameters and tool vibration, and acoustics were considered as responses. Three levels for the parameters were selected, and using Minitab 18 software, 27 trials were generated and experiments were carried out to get the responses. Using Abaqus a numerical model was developed to find the modes of natural frequencies. Then Fast Fourier Transforms to convert the vibration and acoustic characteristics from the time domain to the frequency domain to determine the amplitude closest to the natural frequency of tool vibration and acoustic emission. For the multi-objective optimization, Response Surface Methodology was used to get the optimal combination of machining parameters. Confirmation experiments were carried out at the optimal combinations and the model is well validated.