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An Adaptive Coupling of Edge-Based Smoothed FEM and SPH with a Bidirectional Element-Particle Transformation Algorithm for Laser Powder Bed Fusion.

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Title: An Adaptive Coupling of Edge-Based Smoothed FEM and SPH with a Bidirectional Element-Particle Transformation Algorithm for Laser Powder Bed Fusion.
Authors: Suo, Ming1 (AUTHOR), Long, Ting1 (AUTHOR) longting@gxu.edu.cn
Source: Materials (1996-1944). Jun2026, Vol. 19 Issue 11, p2264. 30p.
Subjects: Powder bed fusion, Finite element method, Thermodynamics, Thermal stresses, Computational fluid dynamics
Abstract: Highlights: An adaptive bidirectional ES-FEM-SPH coupling algorithm with element-particle transformation is proposed for thermo-fluid-solid simulation in laser powder bed fusion. The algorithm includes a nodal mass normalization scheme, a ghost particle coupling algorithm, and a bidirectional transformation algorithm between finite elements and particles. Bidirectional conversion between mesh-free Lagrangian SPH and Lagrangian FEM is realized for the first time with mass conservation. The method enables fully coupled simulation of the temperature field, melt flow dynamics, and thermal stress evolution throughout the LPBF process. Laser powder bed fusion (LPBF) poses significant simulation challenges due to its highly nonlinear thermo-fluid-solid coupling. To address this, we propose an adaptive framework coupling the edge-based smoothed finite element method (ES-FEM) and smoothed particle hydrodynamics (SPH) via a bidirectional element-particle transformation algorithm. This integration leverages ES-FEM for modeling solid thermo-mechanical responses and SPH for resolving melt pool dynamics, enabling fully coupled simulation of temperature, fluid flow, and stress within a unified model. The framework comprises three key components: a nodal mass normalization scheme ensuring conservation during transformations, a ghost particle algorithm for solid-fluid heat transfer and interaction, and a bidirectional finite-element-to-particle conversion mechanism. This work represents the first implementation of bidirectional coupling between mesh-free Lagrangian SPH and Lagrangian FEM. The validation against benchmark cases confirms the framework's accuracy in capturing transient thermal, hydrodynamic, and mechanical behavior. It successfully reproduces key LPBF phenomena, including melt pool morphology, Marangoni flows, and residual stress evolution, demonstrating its suitability for high-fidelity LPBF process simulation. It should be noted that the current ES-FEM-SPH framework has not taken into account the recoil pressure, evaporation, and the interaction between the powder and the molten pool. The powder is regarded as a rigid body. Future work will focus on incorporating these neglected physical factors to further improve the predictive capability of the proposed framework. [ABSTRACT FROM AUTHOR]
Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
  Group: Ti
  Data: An Adaptive Coupling of Edge-Based Smoothed FEM and SPH with a Bidirectional Element-Particle Transformation Algorithm for Laser Powder Bed Fusion.
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  Data: <searchLink fieldCode="AR" term="%22Suo%2C+Ming%22">Suo, Ming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Long%2C+Ting%22">Long, Ting</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> longting@gxu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Materials+%281996-1944%29%22">Materials (1996-1944)</searchLink>. Jun2026, Vol. 19 Issue 11, p2264. 30p.
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  Data: <searchLink fieldCode="DE" term="%22Powder+bed+fusion%22">Powder bed fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Finite+element+method%22">Finite element method</searchLink><br /><searchLink fieldCode="DE" term="%22Thermodynamics%22">Thermodynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Thermal+stresses%22">Thermal stresses</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+fluid+dynamics%22">Computational fluid dynamics</searchLink>
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  Label: Abstract
  Group: Ab
  Data: Highlights: An adaptive bidirectional ES-FEM-SPH coupling algorithm with element-particle transformation is proposed for thermo-fluid-solid simulation in laser powder bed fusion. The algorithm includes a nodal mass normalization scheme, a ghost particle coupling algorithm, and a bidirectional transformation algorithm between finite elements and particles. Bidirectional conversion between mesh-free Lagrangian SPH and Lagrangian FEM is realized for the first time with mass conservation. The method enables fully coupled simulation of the temperature field, melt flow dynamics, and thermal stress evolution throughout the LPBF process. Laser powder bed fusion (LPBF) poses significant simulation challenges due to its highly nonlinear thermo-fluid-solid coupling. To address this, we propose an adaptive framework coupling the edge-based smoothed finite element method (ES-FEM) and smoothed particle hydrodynamics (SPH) via a bidirectional element-particle transformation algorithm. This integration leverages ES-FEM for modeling solid thermo-mechanical responses and SPH for resolving melt pool dynamics, enabling fully coupled simulation of temperature, fluid flow, and stress within a unified model. The framework comprises three key components: a nodal mass normalization scheme ensuring conservation during transformations, a ghost particle algorithm for solid-fluid heat transfer and interaction, and a bidirectional finite-element-to-particle conversion mechanism. This work represents the first implementation of bidirectional coupling between mesh-free Lagrangian SPH and Lagrangian FEM. The validation against benchmark cases confirms the framework's accuracy in capturing transient thermal, hydrodynamic, and mechanical behavior. It successfully reproduces key LPBF phenomena, including melt pool morphology, Marangoni flows, and residual stress evolution, demonstrating its suitability for high-fidelity LPBF process simulation. It should be noted that the current ES-FEM-SPH framework has not taken into account the recoil pressure, evaporation, and the interaction between the powder and the molten pool. The powder is regarded as a rigid body. Future work will focus on incorporating these neglected physical factors to further improve the predictive capability of the proposed framework. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/ma19112264
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      – Code: eng
        Text: English
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        PageCount: 30
        StartPage: 2264
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      – SubjectFull: Powder bed fusion
        Type: general
      – SubjectFull: Finite element method
        Type: general
      – SubjectFull: Thermodynamics
        Type: general
      – SubjectFull: Thermal stresses
        Type: general
      – SubjectFull: Computational fluid dynamics
        Type: general
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      – TitleFull: An Adaptive Coupling of Edge-Based Smoothed FEM and SPH with a Bidirectional Element-Particle Transformation Algorithm for Laser Powder Bed Fusion.
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            NameFull: Suo, Ming
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            NameFull: Long, Ting
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 19
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              Value: 11
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