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<p>Additive manufacturing (AM) commonly yields components with suboptimal surface quality, necessitating post-machining to fulfill strict dimensional and performance requirements. However, the machining process inevitably induces surface and subsurface microstructural evolution, which may affect the integrity and functionality of the final part. This study investigates the microstructural evolution of nickel-based alloy IN625 fabricated by selective laser melting (SLM) during turning operations. The comparative analysis before and after machining reveals significant grain refinement and a marked reduction in dislocation density within the machined surface layer. A dislocation density-based grain refinement model, integrated with the Hall–Petch relationship, was developed and implemented as a user-defined material subroutine. The finite element simulations were performed to predict dislocation density, grain size, and microhardness under first and subsequent cutting passes. The simulation results align well with experimental observations, validating the model’s capability for capturing machining-induced microstructure changes and enabling cost-effective optimization of cutting parameters.</p>

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Machining-induced grain refinement and surface microhardness of IN625 fabricated via selective laser melting: experiments and finite element modeling

  • 期刊论文

  • 发表日期：

    2025年6月16日

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  • Guangyao Li,
  • Binxun Li,
  • Yan Xia,
  • Yujing Sun,
  • Jin Du,
  • Guosheng Su,
  • Shasha Zhou

摘要

Additive manufacturing (AM) commonly yields components with suboptimal surface quality, necessitating post-machining to fulfill strict dimensional and performance requirements. However, the machining process inevitably induces surface and subsurface microstructural evolution, which may affect the integrity and functionality of the final part. This study investigates the microstructural evolution of nickel-based alloy IN625 fabricated by selective laser melting (SLM) during turning operations. The comparative analysis before and after machining reveals significant grain refinement and a marked reduction in dislocation density within the machined surface layer. A dislocation density-based grain refinement model, integrated with the Hall–Petch relationship, was developed and implemented as a user-defined material subroutine. The finite element simulations were performed to predict dislocation density, grain size, and microhardness under first and subsequent cutting passes. The simulation results align well with experimental observations, validating the model’s capability for capturing machining-induced microstructure changes and enabling cost-effective optimization of cutting parameters.