Data

Tools

Indexes

Applications

Experiments

Open Source Science

mouthguards-optimization

This repository serves as preliminary dataset created with synthetic data for theoretical experimentation on the use of artificial neural netoworks to optimize resins and structural design of mouthguards.

https://github.com/drmateusrocha/mouthguards-optimization

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (1.3%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

This repository serves as preliminary dataset created with synthetic data for theoretical experimentation on the use of artificial neural netoworks to optimize resins and structural design of mouthguards.

Basic Info
  • Host: GitHub
  • Owner: drmateusrocha
  • Default Branch: main
  • Size: 8.79 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed about 1 year ago
Metadata Files
Readme Citation

README.md

mouthguards-optimization

This repository serves as preliminary dataset created with synthetic data for theoretical experimentation on the use of artificial neural netoworks to optimize resins and structural design of mouthguards.

Owner

  • Name: Mateus Rocha
  • Login: drmateusrocha
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
title: >-
  Optimizing 3D-Printed Mouthguard Materials Using
  Artificial Intelligence
type: dataset
authors:
  - given-names: Mateus Garcia
    family-names: Rocha
    email: mrocha@dental.ufl.edu
    affiliation: University of Florida College of Dentistry
    orcid: 'https://orcid.org/0000-0001-5658-5640'
  - given-names: Jason
    family-names: Griggs
    email: jgriggs@umc.edu
repository-code: 'https://github.com/drmateusrocha/mouthguards-optimization'
abstract: >-
  In this theoretical project, synthetic data serves as a
  preliminary testbed for exploring the feasibility of AI/ML
  frameworks in addressing sports-related orofacial
  injuries, a concern given the 30 million young sports
  participants annually. Current mouthguards, although
  crucial, lack efficient energy absorption, presenting a
  challenge that 3D printing technology aims to solve by
  engineering resilient internal structures. The quest for
  optimal 3D-printing resins and innovative mouthguard
  designs is spearheaded by leveraging artificial
  intelligence and machine learning to refine polyurethane
  resin formulations for 3D printing, and devising
  mouthguard designs with internal 3D-printed lattice
  structures for better kinetic energy absorption. Through a
  mix of in vitro and in silico testing, the project intends
  to evaluate the mechanical and energy absorption
  properties of the optimized resins and designs. The end
  goal is to fabricate mouthguards offering enhanced athlete
  protection and minimizing oral and craniofacial sports
  injuries.
keywords:
  - Dental Biomaterials
  - 3D-Printing
  - Mouthguards
  - Artificial Intelligence
  - Finite Element Analysis

GitHub Events

Total
  • Push event: 1
Last Year
  • Push event: 1