REVOLUTIONIZING 3D PRINTING THROUGH MACHINE LEARNING : POTENTIAL AND CHALLENGES IN BIOPRINTING

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10258453" target="_blank" >RIV/61989100:27230/25:10258453 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001499599200001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001499599200001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.17973/MMSJ.2025_06_2025055" target="_blank" >10.17973/MMSJ.2025_06_2025055</a>

Jazyk výsledku

angličtina

Název v původním jazyce

REVOLUTIONIZING 3D PRINTING THROUGH MACHINE LEARNING : POTENTIAL AND CHALLENGES IN BIOPRINTING

Popis výsledku v původním jazyce

Recent advancements in three dimensional (3D) printing technologies have transformed both industrial practices and everyday applications. In the biomedical domain, 3D bioprinting at the cellular and tissue levels has emerged as a promising approach with significant potential. Although machine learning (ML) has been successfully applied in various aspects of conventional 3D printing, including process optimization, dimensional accuracy analysis, defect detection, and material property prediction, its adoption in the context of 3D bioprinting remains limited. This review examines the current ML techniques used in traditional 3D printing and explores their potential contributions to the development of bioprinting technologies. Notably, existing studies have demonstrated up to a 25% improvement in dimensional accuracy and a 30% reduction in printing time when ML is applied to scaffold optimization. We argue that the integration of ML could significantly influence the future development of 3D bioprinting, opening new avenues for innovation in biomedical engineering.

Název v anglickém jazyce

REVOLUTIONIZING 3D PRINTING THROUGH MACHINE LEARNING : POTENTIAL AND CHALLENGES IN BIOPRINTING

Popis výsledku anglicky

Recent advancements in three dimensional (3D) printing technologies have transformed both industrial practices and everyday applications. In the biomedical domain, 3D bioprinting at the cellular and tissue levels has emerged as a promising approach with significant potential. Although machine learning (ML) has been successfully applied in various aspects of conventional 3D printing, including process optimization, dimensional accuracy analysis, defect detection, and material property prediction, its adoption in the context of 3D bioprinting remains limited. This review examines the current ML techniques used in traditional 3D printing and explores their potential contributions to the development of bioprinting technologies. Notably, existing studies have demonstrated up to a 25% improvement in dimensional accuracy and a 30% reduction in printing time when ML is applied to scaffold optimization. We argue that the integration of ML could significantly influence the future development of 3D bioprinting, opening new avenues for innovation in biomedical engineering.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20300 - Mechanical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

MM Science Journal

ISSN

1803-1269

e-ISSN

1805-0476

Svazek periodika

2025

Číslo periodika v rámci svazku

2025

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

6

Strana od-do

8436-8441

Kód UT WoS článku

001499599200001

EID výsledku v databázi Scopus

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