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<p>This work aims to present a robust bi-objective optimization analysis in the end milling of duplex stainless steel UNS S32205. The study included design variables (cutting speed, feed per tooth, cutting depth, work penetration) and noise variables (flank wear, fluid flow, tool overhang length) to achieve results that are closer to the reality of the process. Response surface methodology and normal boundary intersection were applied to evaluate Ra roughness and material removal rate (MRR). The results showed that work penetration, cutting depth, and tool overhang length have no significant impact on Ra. Regarding MRR, only tool flank wear showed no significance for the response. Bi-objective optimization was performed, and a Pareto’s front was found with several optimal configurations, achieving up to a 50% improvement in the studied responses. This paper presents something unprecedented in the literature to date by analyzing the noise of the end milling process using UNS S32205.</p>

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Robust optimization analysis of the end milling of duplex stainless steel UNS S32205

  • 期刊论文

  • 发表日期：

    2025年1月13日

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  • Guilherme Augusto Vilas Boas Vasconcelos,
  • Matheus Brendon Francisco,
  • Carlos Henrique de Oliveira,
  • Tarcísio Gonçalves de Brito,
  • João Roberto Ferreira

摘要

This work aims to present a robust bi-objective optimization analysis in the end milling of duplex stainless steel UNS S32205. The study included design variables (cutting speed, feed per tooth, cutting depth, work penetration) and noise variables (flank wear, fluid flow, tool overhang length) to achieve results that are closer to the reality of the process. Response surface methodology and normal boundary intersection were applied to evaluate Ra roughness and material removal rate (MRR). The results showed that work penetration, cutting depth, and tool overhang length have no significant impact on Ra. Regarding MRR, only tool flank wear showed no significance for the response. Bi-objective optimization was performed, and a Pareto’s front was found with several optimal configurations, achieving up to a 50% improvement in the studied responses. This paper presents something unprecedented in the literature to date by analyzing the noise of the end milling process using UNS S32205.