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Adaptive Neuro-Fuzzy-Based Models for Predicting the Tribological Properties of 3D-Printed PLA Green Composites Used for Biomedical Applications

Tribological performance is a critical aspect of materials used in biomedical applications, as it can directly impact the comfort and functionality of devices for individuals with disabilities. Polylactic Acid (PLA) is a widely used 3D-printed material in this field, but its mechanical and tribologi...

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Autores principales: Albahkali, Thamer, Abdo, Hany S., Salah, Omar, Fouly, Ahmed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383854/
https://www.ncbi.nlm.nih.gov/pubmed/37514443
http://dx.doi.org/10.3390/polym15143053
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author Albahkali, Thamer
Abdo, Hany S.
Salah, Omar
Fouly, Ahmed
author_facet Albahkali, Thamer
Abdo, Hany S.
Salah, Omar
Fouly, Ahmed
author_sort Albahkali, Thamer
collection PubMed
description Tribological performance is a critical aspect of materials used in biomedical applications, as it can directly impact the comfort and functionality of devices for individuals with disabilities. Polylactic Acid (PLA) is a widely used 3D-printed material in this field, but its mechanical and tribological properties can be limiting. This study focuses on the development of an artificial intelligence model using ANFIS to predict the wear volume of PLA composites under various conditions. The model was built on data gathered from tribological experiments involving PLA green composites with different weight fractions of date particles. These samples were annealed for different durations to eliminate residual stresses from 3D printing and then subjected to tribological tests under varying normal loads and sliding distances. Mechanical properties and finite element models were also analyzed to better understand the tribological results and evaluate the load-carrying capacity of the PLA composites. The ANFIS model demonstrated excellent compatibility and robustness in predicting wear volume, with an average percentage error of less than 0.01% compared to experimental results. This study highlights the potential of heat-treated PLA green composites for improved tribological performance in biomedical applications.
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spelling pubmed-103838542023-07-30 Adaptive Neuro-Fuzzy-Based Models for Predicting the Tribological Properties of 3D-Printed PLA Green Composites Used for Biomedical Applications Albahkali, Thamer Abdo, Hany S. Salah, Omar Fouly, Ahmed Polymers (Basel) Article Tribological performance is a critical aspect of materials used in biomedical applications, as it can directly impact the comfort and functionality of devices for individuals with disabilities. Polylactic Acid (PLA) is a widely used 3D-printed material in this field, but its mechanical and tribological properties can be limiting. This study focuses on the development of an artificial intelligence model using ANFIS to predict the wear volume of PLA composites under various conditions. The model was built on data gathered from tribological experiments involving PLA green composites with different weight fractions of date particles. These samples were annealed for different durations to eliminate residual stresses from 3D printing and then subjected to tribological tests under varying normal loads and sliding distances. Mechanical properties and finite element models were also analyzed to better understand the tribological results and evaluate the load-carrying capacity of the PLA composites. The ANFIS model demonstrated excellent compatibility and robustness in predicting wear volume, with an average percentage error of less than 0.01% compared to experimental results. This study highlights the potential of heat-treated PLA green composites for improved tribological performance in biomedical applications. MDPI 2023-07-15 /pmc/articles/PMC10383854/ /pubmed/37514443 http://dx.doi.org/10.3390/polym15143053 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Albahkali, Thamer
Abdo, Hany S.
Salah, Omar
Fouly, Ahmed
Adaptive Neuro-Fuzzy-Based Models for Predicting the Tribological Properties of 3D-Printed PLA Green Composites Used for Biomedical Applications
title Adaptive Neuro-Fuzzy-Based Models for Predicting the Tribological Properties of 3D-Printed PLA Green Composites Used for Biomedical Applications
title_full Adaptive Neuro-Fuzzy-Based Models for Predicting the Tribological Properties of 3D-Printed PLA Green Composites Used for Biomedical Applications
title_fullStr Adaptive Neuro-Fuzzy-Based Models for Predicting the Tribological Properties of 3D-Printed PLA Green Composites Used for Biomedical Applications
title_full_unstemmed Adaptive Neuro-Fuzzy-Based Models for Predicting the Tribological Properties of 3D-Printed PLA Green Composites Used for Biomedical Applications
title_short Adaptive Neuro-Fuzzy-Based Models for Predicting the Tribological Properties of 3D-Printed PLA Green Composites Used for Biomedical Applications
title_sort adaptive neuro-fuzzy-based models for predicting the tribological properties of 3d-printed pla green composites used for biomedical applications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383854/
https://www.ncbi.nlm.nih.gov/pubmed/37514443
http://dx.doi.org/10.3390/polym15143053
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