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<p>The long-term performance of total hip replacement (THR) implants is strongly influenced by their mechanical behavior under physiological loading and the resulting load transfer to surrounding bone. This study presents a finite element–based multi-objective optimization framework aimed at improving the biomechanical performance of cementless femoral stems by simultaneously refining three interdependent geometric parameters: horizontal offset (HO), vertical offset (VO), and neck-shaft angle (NSA). Twenty-seven stem configurations were evaluated under four functional loading scenarios, standing, sitting, walking, and stair climbing, to assess von Mises stress distribution, femoral bone strain, and relative micromotion at the bone–implant interface. Response Surface Methodology (RSM) combined with a desirability function approach was employed to identify an optimal configuration (HO = 34.2&#xa0;mm, VO = 42.2&#xa0;mm, NSA = 135.3°), which was subsequently compared with a non-optimized reference design. The optimized configuration demonstrated consistently improved biomechanical indicators across all activities. During stair climbing, maximum bone strain was reduced from 2.47% to 0.9%, and interface micromotion remained below 50&#xa0;μm, whereas the reference configuration frequently exceeded this level. Load transfer patterns indicated improved strain distribution along the diaphyseal region, with reduced proximal and distal concentration. A two-year mechanically driven bone remodeling simulation further suggested more favorable peri-implant mechanical stimulus distribution in the optimized design. These results highlight the potential of integrating multi-activity loading and multi-parameter optimization within a unified computational framework for femoral stem design. Nevertheless, experimental validation and patient-specific investigations are required to confirm the translational applicability of the proposed methodology.</p> Graphical abstract <p></p>

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Finite element-based multi-objective optimization of femoral stem geometry for biomechanical stability across functional activities

  • 期刊论文

  • 发表日期：

    2026年4月10日

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  • Sameh Elleuch,
  • Rihem Nouira,
  • Hanen Jrad

摘要

The long-term performance of total hip replacement (THR) implants is strongly influenced by their mechanical behavior under physiological loading and the resulting load transfer to surrounding bone. This study presents a finite element–based multi-objective optimization framework aimed at improving the biomechanical performance of cementless femoral stems by simultaneously refining three interdependent geometric parameters: horizontal offset (HO), vertical offset (VO), and neck-shaft angle (NSA). Twenty-seven stem configurations were evaluated under four functional loading scenarios, standing, sitting, walking, and stair climbing, to assess von Mises stress distribution, femoral bone strain, and relative micromotion at the bone–implant interface. Response Surface Methodology (RSM) combined with a desirability function approach was employed to identify an optimal configuration (HO = 34.2 mm, VO = 42.2 mm, NSA = 135.3°), which was subsequently compared with a non-optimized reference design. The optimized configuration demonstrated consistently improved biomechanical indicators across all activities. During stair climbing, maximum bone strain was reduced from 2.47% to 0.9%, and interface micromotion remained below 50 μm, whereas the reference configuration frequently exceeded this level. Load transfer patterns indicated improved strain distribution along the diaphyseal region, with reduced proximal and distal concentration. A two-year mechanically driven bone remodeling simulation further suggested more favorable peri-implant mechanical stimulus distribution in the optimized design. These results highlight the potential of integrating multi-activity loading and multi-parameter optimization within a unified computational framework for femoral stem design. Nevertheless, experimental validation and patient-specific investigations are required to confirm the translational applicability of the proposed methodology.

Graphical abstract