REVOLUTIONIZING 3D PRINTING THROUGH MACHINE LEARNING : POTENTIAL AND CHALLENGES IN BIOPRINTING
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
REVOLUTIONIZING 3D PRINTING THROUGH MACHINE LEARNING : POTENTIAL AND CHALLENGES IN BIOPRINTING
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20300 - Mechanical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
MM Science Journal
ISSN
1803-1269
e-ISSN
1805-0476
Volume of the periodical
2025
Issue of the periodical within the volume
2025
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
6
Pages from-to
8436-8441
UT code for WoS article
001499599200001
EID of the result in the Scopus database
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