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<p>This paper focuses on optimising two key machining outcomes—surface roughness (Ra) and material removal rate (MRR)—during the milling of two industrial-grade steels, Z200C12 and Z30C13. The contribution of cutting parameters—rotation (N), feed rate (f), and depth of cut (ap) on the workpiece surface—was, therefore, investigated by experimentation according to both the unifactorial and multifactorial approaches. Analysis of variance (ANOVA) showed that, for Z200C12, the depth of cut had the greatest effect on Ra, whereas spindle speed was the dominant factor influencing surface finish in Z30C13. Regression models with high R<sup>2</sup> values (&gt; 99% for Ra, &gt; 92% for MRR) showed high prediction accuracy for machining responses. A multi-objective optimisation strategy using the desirability approach was carried out to minimise Ra and maximise MRR (the best parameter setting of N = 1547&#xa0;rpm, f = 150&#xa0;mm/min, and ap = 0.5&#xa0;mm was obtained). In this circumstance, Z200C12 obtained the best surface quality (Ra = 0.637&#xa0;μm) and the highest MRR (7 cm<sup>3</sup>/min) with a desirability value of 0.64. These results indicate that Z200C12 is more suitable for applications requiring high surface quality in combination with machining productivity.</p>

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Multi-objective optimisation of milling parameters for Z200C12 and Z30C13 steels using ANOVA and desirability functions

  • 期刊论文

  • 发表日期：

    2025年8月18日

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  • Slimane Benchiheub,
  • Mohamed Faouzi Bacha

摘要

This paper focuses on optimising two key machining outcomes—surface roughness (Ra) and material removal rate (MRR)—during the milling of two industrial-grade steels, Z200C12 and Z30C13. The contribution of cutting parameters—rotation (N), feed rate (f), and depth of cut (ap) on the workpiece surface—was, therefore, investigated by experimentation according to both the unifactorial and multifactorial approaches. Analysis of variance (ANOVA) showed that, for Z200C12, the depth of cut had the greatest effect on Ra, whereas spindle speed was the dominant factor influencing surface finish in Z30C13. Regression models with high R2 values (> 99% for Ra, > 92% for MRR) showed high prediction accuracy for machining responses. A multi-objective optimisation strategy using the desirability approach was carried out to minimise Ra and maximise MRR (the best parameter setting of N = 1547 rpm, f = 150 mm/min, and ap = 0.5 mm was obtained). In this circumstance, Z200C12 obtained the best surface quality (Ra = 0.637 μm) and the highest MRR (7 cm3/min) with a desirability value of 0.64. These results indicate that Z200C12 is more suitable for applications requiring high surface quality in combination with machining productivity.