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Purpose <p>This study investigates the influence of key cutting parameters—feed per revolution (f), cutting speed (Vc), depth of cut (ap), and tool edge radius (r)—on surface roughness (Ra), tangential vibrations (Az), material removal rate (MRR), and sound intensity (Is) during the intermittent turning of AISI D3 steel.</p> Methods <p>A Taguchi L9 (34) orthogonal array was employed to design the experiments. Turning operations were conducted using a triple-coated CVD carbide cutting tool (Al2O3/TiC/TiCN). Analysis of Variance (ANOVA) was utilized to assess the significance of each cutting parameter on the output responses. Mathematical models were developed using Response Surface Methodology (RSM). Multi-objective optimization was performed using three Multi-Criteria Decision-Making (MCDM) techniques: TOPSIS, COCOSO, and EAMR.</p> Results <p>The TOPSIS and EDAS methods effectively maximized material removal rate (MRR), while the COCOSO method successfully minimized sound intensity (Is). The EAMR approach provided an optimal configuration of input variables, achieving superior surface quality (Ra) and minimized vibrations (Az).</p> Conclusion <p>Integrating statistical modeling with MCDM techniques offers a robust framework for optimizing cutting parameters in intermittent turning operations. The findings provide valuable insights for enhancing machining performance, particularly when working with hard-to-machine materials like AISI D3 steel.</p>

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Multi-Objective Optimization in Intermittent Turning: Balancing Productivity, Surface Quality, Sound Intensity, and Tool Vibration Using MCDM Methods

  • 期刊论文

  • 发表日期：

    2025年5月14日

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  • Mohammed Athmane Yallese,
  • Septi Boucherit,
  • Fethi Khelfaoui,
  • Zakaria Ouelaa,
  • Sabrina Haoues,
  • Salim Belhadi

摘要

Purpose

This study investigates the influence of key cutting parameters—feed per revolution (f), cutting speed (Vc), depth of cut (ap), and tool edge radius (r)—on surface roughness (Ra), tangential vibrations (Az), material removal rate (MRR), and sound intensity (Is) during the intermittent turning of AISI D3 steel.

Methods

A Taguchi L9 (34) orthogonal array was employed to design the experiments. Turning operations were conducted using a triple-coated CVD carbide cutting tool (Al2O3/TiC/TiCN). Analysis of Variance (ANOVA) was utilized to assess the significance of each cutting parameter on the output responses. Mathematical models were developed using Response Surface Methodology (RSM). Multi-objective optimization was performed using three Multi-Criteria Decision-Making (MCDM) techniques: TOPSIS, COCOSO, and EAMR.

Results

The TOPSIS and EDAS methods effectively maximized material removal rate (MRR), while the COCOSO method successfully minimized sound intensity (Is). The EAMR approach provided an optimal configuration of input variables, achieving superior surface quality (Ra) and minimized vibrations (Az).

Conclusion

Integrating statistical modeling with MCDM techniques offers a robust framework for optimizing cutting parameters in intermittent turning operations. The findings provide valuable insights for enhancing machining performance, particularly when working with hard-to-machine materials like AISI D3 steel.