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Background <p>The machining process of AISI 1040 steel involves complex interactions among cutting parameters, which significantly impact surface roughness (Ra) and vibration accelerations (Ax and Ay). Understanding and optimizing these factors are critical for enhancing manufacturing efficiency, tool life, and product quality.</p> Purpose <p>This study aims to develop mathematical models for combined optimization of vibration accelerations (Ax, Ay) and surface roughness (Ra) in the milling of AISI 1040 steel using GC1030 coated carbide tools, thereby providing practical guidance for industrial applications.</p> Methods <p>The experimental design was based on a Box–Behnken design integrated with Surface Response Methodology (SRM) and Analysis of Variance (ANOVA). The effects of manufacturing speed (Vc), tooth advancement (fz), and pass height (ap) were modeled through unifactorial and multifactorial approaches to identify optimal machining conditions.</p> Results <p>Cutting speed (Vc) was identified as the most influential factor affecting vibrations and surface roughness. The optimal machining parameters were determined as Vc = 91.153 m/min, fz = 0.040 mm/tooth, and ap = 0.50 mm, resulting in minimal vibration accelerations and improved surface quality. The developed mathematical models demonstrated high predictive accuracy and industrial applicability.</p> Conclusion <p>The proposed models provide a robust framework for optimizing milling processes, enabling engineers and production managers to achieve improved efficiency, extended tool life, and superior part quality. These findings underscore the models' value in enhancing decision-making in the manufacturing sector.</p>

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Optimization of Manufacturing Parameters for Minimizing Vibrations and Surface Roughness in Milling Using Box–Behnken Design

  • 期刊论文

  • 发表日期：

    2025年1月6日

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  • Mohamed Fnides,
  • Salah Amroune,
  • Mohamed Slamani,
  • Abdelmalek Elhadi,
  • Mustapha Arslane,
  • Mohammad Jawaid

摘要

Background

The machining process of AISI 1040 steel involves complex interactions among cutting parameters, which significantly impact surface roughness (Ra) and vibration accelerations (Ax and Ay). Understanding and optimizing these factors are critical for enhancing manufacturing efficiency, tool life, and product quality.

Purpose

This study aims to develop mathematical models for combined optimization of vibration accelerations (Ax, Ay) and surface roughness (Ra) in the milling of AISI 1040 steel using GC1030 coated carbide tools, thereby providing practical guidance for industrial applications.

Methods

The experimental design was based on a Box–Behnken design integrated with Surface Response Methodology (SRM) and Analysis of Variance (ANOVA). The effects of manufacturing speed (Vc), tooth advancement (fz), and pass height (ap) were modeled through unifactorial and multifactorial approaches to identify optimal machining conditions.

Results

Cutting speed (Vc) was identified as the most influential factor affecting vibrations and surface roughness. The optimal machining parameters were determined as Vc = 91.153 m/min, fz = 0.040 mm/tooth, and ap = 0.50 mm, resulting in minimal vibration accelerations and improved surface quality. The developed mathematical models demonstrated high predictive accuracy and industrial applicability.

Conclusion

The proposed models provide a robust framework for optimizing milling processes, enabling engineers and production managers to achieve improved efficiency, extended tool life, and superior part quality. These findings underscore the models' value in enhancing decision-making in the manufacturing sector.