Exploring the Capacity of a Generative Design Software Application Based on AI Applied to the Design of Sports Foot Prostheses

Thanks to the development of Artificial Intelligence in Computational Materials Science., new software applications can predict the behavior of materials when subjected to different conditions. In Product Design., these computational tools are of great help because they offer users different alternatives when selecting materials to ensure the durability of their designs. Combining the advances of Artificial Intelligence., Computational Materials Science., and Product Design., various software applications emerge that contain these algorithms programmed to use Generative Design. This article explores the scopes of a software tool application based on Generative Design., which is applied to developing lower limb prostheses aimed at being used by children since the geometries obtained can be attractive to them and reduce the adverse effects of their perception when using them. A case study describes the methodology employed to design this type of prosthesis. The results found that the software application used for the Generative Design is remarkably user-friendly., providing a sense of reassurance and confidence in its usability. Another result was that the boundary conditions were identified to design different kinds of prostheses., in addition to the results offered by the application software.

Authors

Institutions

Publication Details

Published
2024-11-23
DOI
https://doi.org/10.1109/icngn63705.2024.10871269
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring the Capacity of a Generative Design Software Application Based on AI Applied to the Design of Sports Foot Prostheses

Héctor Rafael Morano-Okuno, J. Enrique Chong-Quero, Guillermo Sandoval-Benitez, Donovan M. Esqueda-Merino et al.
Hand Gesture Recognition Systems
article

Exploring the Capacity of a Generative Design Software Application Based on AI Applied to the Design of Sports Foot Prostheses

Héctor Rafael Morano-Okuno, J. Enrique Chong-Quero, Guillermo Sandoval-Benitez, Donovan M. Esqueda-Merino, Ricardo Jaramillo-Godinez, Daishi Alfredo Murano-Labastida
article en

Abstract

Thanks to the development of Artificial Intelligence in Computational Materials Science., new software applications can predict the behavior of materials when subjected to different conditions. In Product Design., these computational tools are of great help because they offer users different alternatives when selecting materials to ensure the durability of their designs. Combining the advances of Artificial Intelligence., Computational Materials Science., and Product Design., various software applications emerge that contain these algorithms programmed to use Generative Design. This article explores the scopes of a software tool application based on Generative Design., which is applied to developing lower limb prostheses aimed at being used by children since the geometries obtained can be attractive to them and reduce the adverse effects of their perception when using them. A case study describes the methodology employed to design this type of prosthesis. The results found that the software application used for the Generative Design is remarkably user-friendly., providing a sense of reassurance and confidence in its usability. Another result was that the boundary conditions were identified to design different kinds of prostheses., in addition to the results offered by the application software.

Tecnológico de Monterrey (MX)
Openalex Percentile: Top 73%
Hand Gesture Recognition Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.