Structural analysis of generative design applied to ergonomic plantar orthoses
DOI:
https://doi.org/10.37636/recit.v9n2e448Keywords:
Finite element analysis, Generative design, Ergonomics, Elastomeric materialsAbstract
Plantar orthoses are devices designed to provide support and correct the biomechanics of the foot. Generative design offers ample potential for personalization; however, the analysis of its structural behavior continues to be a significant challenge. This research aims to evaluate the structural optimization of orthoses designed by generative design compared to traditional models. An analysis of 33 middle-aged adult men classified as normal weight, with an average weight of 65.32 ± 6.79 kg, was performed using a baropodometric database. An optimized orthosis was designed by parametric modeling to evaluate its mechanical response in static standing conditions, using the finite element method with the TPU A95 material. The results indicated that the trabecular structures produced by generative design absorb more energy (0.3876 J) than a bilaminated orthosis made with EVA A40 and A15 materials (0.0362 J). The levels of deformation obtained (maximum principal strain = 1.34%, equivalent elastic strain = 2.14%) indicate that the composition of the generative model works well within the elastic regime, ensuring structural integrity. However, the low strain and strain energy values suggest relatively rigid behavior, which can limit the shock absorption capacity. The main contribution of this work is to demonstrate how generative design can be integrated into methodologies for designing plantar orthotics. It explores the potential benefits of this approach and examines how generative design parameters influence mechanical responses. This research provides a technical foundation for optimizing ergonomic orthoses through generative design and structural modeling. The findings emphasize the potential of generative design in creating optimized orthoses and highlight the significance of design parameters on the outcomes achieved. This insight is valuable for future applications of generative design in the field of ergonomics.
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Copyright (c) 2026 Christian Enrique Nava-Alcantar, Agustín Vidal-Lesso, Marco Antonio Martínez-Bocanegra, Luis Ángel Ortiz-Lango, Juan Carlos García-Valadez, Sergio Alonso-Romero, Israel Miguel-Andres

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