A Neural Network-Accelerated Approach for Orthopedic Implant Design and Evaluation Through Strain Shielding Analysis DOI Creative Commons
Ana Pais, Jorge Lino Alves, J. Belinha

и другие.

Biomimetics, Год журнала: 2025, Номер 10(4), С. 238 - 238

Опубликована: Апрель 13, 2025

The design of orthopedic implants is a complex challenge, requiring the careful balance mechanical performance and biological integration to ensure long-term success. This study focuses on development porous femoral stem implant aimed at reducing stiffness mitigating stress shielding effects. To accelerate process, neural networks were trained predict optimal density distribution implant, enabling rapid optimization. Two initial spaces evaluated, revealing necessity incorporating femur’s anatomical features into process. models achieved median error near 0 for both conventional extended spaces, producing optimized designs in fraction computational time typically required. Finite element analysis (FEA) was employed assess network-generated implants. results demonstrated that network predictions effectively reduced compared solid model 50% test cases. While graded porosity did not show significant differences prevention uniform design, it found be significantly stronger, highlighting its potential enhanced durability. work underscores efficacy network-accelerated improving efficiency performance.

Язык: Английский

A Neural Network-Accelerated Approach for Orthopedic Implant Design and Evaluation Through Strain Shielding Analysis DOI Creative Commons
Ana Pais, Jorge Lino Alves, J. Belinha

и другие.

Biomimetics, Год журнала: 2025, Номер 10(4), С. 238 - 238

Опубликована: Апрель 13, 2025

The design of orthopedic implants is a complex challenge, requiring the careful balance mechanical performance and biological integration to ensure long-term success. This study focuses on development porous femoral stem implant aimed at reducing stiffness mitigating stress shielding effects. To accelerate process, neural networks were trained predict optimal density distribution implant, enabling rapid optimization. Two initial spaces evaluated, revealing necessity incorporating femur’s anatomical features into process. models achieved median error near 0 for both conventional extended spaces, producing optimized designs in fraction computational time typically required. Finite element analysis (FEA) was employed assess network-generated implants. results demonstrated that network predictions effectively reduced compared solid model 50% test cases. While graded porosity did not show significant differences prevention uniform design, it found be significantly stronger, highlighting its potential enhanced durability. work underscores efficacy network-accelerated improving efficiency performance.

Язык: Английский

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