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<p>This review study rigorously analyses the progress in additive manufacturing technologies, explicitly addressing the impact of process parameters on the material properties, such as a change of 20% to 30% in tensile strength of fabricated components compared to traditionally manufactured parts due to anisotropy. It examines the present condition of material modelling techniques for precisely simulating 3D-printed structures, particularly within the Finite Element Analysis (FEA) framework. The review employs an analytical framework to assess current procedures, highlighting the interaction between material characterisation, parameter-driven anisotropy, and the significance of meshing techniques in attaining simulation accuracy. The work delineates practical meshing algorithms and virtually precise material models, providing a straightforward approach to minimize computational effort while improving forecast accuracy. Nonetheless, obstacles remain, such as high-dimensional parameter spaces, material variability, and insufficient data for novel materials. Integrating machine learning, real-time monitoring, and digital twin technologies presents interesting avenues for enhancing predictive modelling capabilities in additive manufacturing. These insights are invaluable for industry and academic communities seeking to enhance Additive Manufacturing for critical applications, especially aerospace and defence.</p>

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Virtually Understanding the Reality: a Review of the Finite Element Simulation of Additively Manufactured Polymer Parts

  • 期刊论文

  • 发表日期：

    2025年10月10日

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  • Nishank Verma,
  • Mythravaruni Pullela

摘要

This review study rigorously analyses the progress in additive manufacturing technologies, explicitly addressing the impact of process parameters on the material properties, such as a change of 20% to 30% in tensile strength of fabricated components compared to traditionally manufactured parts due to anisotropy. It examines the present condition of material modelling techniques for precisely simulating 3D-printed structures, particularly within the Finite Element Analysis (FEA) framework. The review employs an analytical framework to assess current procedures, highlighting the interaction between material characterisation, parameter-driven anisotropy, and the significance of meshing techniques in attaining simulation accuracy. The work delineates practical meshing algorithms and virtually precise material models, providing a straightforward approach to minimize computational effort while improving forecast accuracy. Nonetheless, obstacles remain, such as high-dimensional parameter spaces, material variability, and insufficient data for novel materials. Integrating machine learning, real-time monitoring, and digital twin technologies presents interesting avenues for enhancing predictive modelling capabilities in additive manufacturing. These insights are invaluable for industry and academic communities seeking to enhance Additive Manufacturing for critical applications, especially aerospace and defence.