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<p>Superduplex stainless steels are widely employed in the oil and gas industry due to their exceptional mechanical strength, corrosion resistance and ductility. However, they present low machinability due to high hardness and tendency to work harden. This work presents a numerical-experimental approach to predict cutting forces in superduplex steel machining using data from its individual phases, represented by austenitic (AISI 316) and ferritic (AISI 410) stainless steels. A numerical model based on limit analysis theory and finite element simulations is employed, considering input parameters such as tool geometry, feed rate and friction coefficient. The friction coefficient at the chip-tool interface is estimated through a two-stage process: experimental analysis of AISI 316 and 410 steels followed by a linear interpolation model to predict values for superduplex steel. The proposed method significantly reduces the experimental requirements by taking advantage of microstructural similarities and demonstrates the feasibility of obtaining accurate numerical cut force predictions. This study highlights a cost-effective strategy for the analysis of machining in complex materials by integrating experimental data, mechanistic modeling and numerical simulation.</p>

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Predicting cutting forces in superduplex stainless steels via phase-equivalent materials and finite element modeling

  • 期刊论文

  • 发表日期：

    2025年12月18日

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  • Fabio Figueiredo,
  • Luisa Gomes,
  • Adriane Mougo,
  • Livia Nogueira

摘要

Superduplex stainless steels are widely employed in the oil and gas industry due to their exceptional mechanical strength, corrosion resistance and ductility. However, they present low machinability due to high hardness and tendency to work harden. This work presents a numerical-experimental approach to predict cutting forces in superduplex steel machining using data from its individual phases, represented by austenitic (AISI 316) and ferritic (AISI 410) stainless steels. A numerical model based on limit analysis theory and finite element simulations is employed, considering input parameters such as tool geometry, feed rate and friction coefficient. The friction coefficient at the chip-tool interface is estimated through a two-stage process: experimental analysis of AISI 316 and 410 steels followed by a linear interpolation model to predict values for superduplex steel. The proposed method significantly reduces the experimental requirements by taking advantage of microstructural similarities and demonstrates the feasibility of obtaining accurate numerical cut force predictions. This study highlights a cost-effective strategy for the analysis of machining in complex materials by integrating experimental data, mechanistic modeling and numerical simulation.