Machine Learning in Gel-Based Additive Manufacturing: From Material Design to Process Optimization
Sábháilte in:
| Foilsithe in: | Gels vol. 11, no. 8 (2025), p. 582-610 |
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| Príomhchruthaitheoir: | |
| Rannpháirtithe: | , |
| Foilsithe / Cruthaithe: |
MDPI AG
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| Ábhair: | |
| Rochtain ar líne: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3244038758 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2310-2861 | ||
| 024 | 7 | |a 10.3390/gels11080582 |2 doi | |
| 035 | |a 3244038758 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Zhang Zhizhou |u Department of Mechanical and Aerospace Engineering, School of Engineering, The University of Manchester, Manchester M13 9PL, UK | |
| 245 | 1 | |a Machine Learning in Gel-Based Additive Manufacturing: From Material Design to Process Optimization | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Machine learning is reshaping gel-based additive manufacturing by enabling accelerated material design and predictive process optimization. This review provides a comprehensive overview of recent progress in applying machine learning across gel formulation development, printability prediction, and real-time process control. The integration of algorithms such as neural networks, random forests, and support vector machines allows accurate modeling of gel properties, including rheology, elasticity, swelling, and viscoelasticity, from compositional and processing data. Advances in data-driven formulation and closed-loop robotics are moving gel printing from trial and error toward autonomous and efficient material discovery. Despite these advances, challenges remain regarding data sparsity, model robustness, and integration with commercial printing systems. The review results highlight the value of open-source datasets, standardized protocols, and robust validation practices to ensure reproducibility and reliability in both research and clinical environments. Looking ahead, combining multimodal sensing, generative design, and automated experimentation will further accelerate discoveries and enable new possibilities in tissue engineering, biomedical devices, soft robotics, and sustainable materials manufacturing. | |
| 653 | |a Mechanical properties | ||
| 653 | |a Behavior | ||
| 653 | |a Data processing | ||
| 653 | |a Optimization | ||
| 653 | |a Closed loops | ||
| 653 | |a Amino acids | ||
| 653 | |a Viscoelasticity | ||
| 653 | |a Manufacturing | ||
| 653 | |a Machine learning | ||
| 653 | |a Additive manufacturing | ||
| 653 | |a Industrial robots | ||
| 653 | |a Viscosity | ||
| 653 | |a Neural networks | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Support vector machines | ||
| 653 | |a Rheology | ||
| 653 | |a Automation | ||
| 653 | |a Soft robotics | ||
| 653 | |a Lasers | ||
| 653 | |a Rheological properties | ||
| 653 | |a Biomedical engineering | ||
| 653 | |a Yield stress | ||
| 653 | |a Interfacial bonding | ||
| 653 | |a Tissue engineering | ||
| 653 | |a Real time | ||
| 653 | |a Design optimization | ||
| 700 | 1 | |a Wang, Yaxin |u Centre for the Cellular Microenvironment (CeMi), University of Glasgow, Glasgow G12 8QQ, UK | |
| 700 | 1 | |a Wang, Weiguang |u Department of Mechanical Engineering, School of Engineering, University of Southampton, Southampton SO17 1BJ, UK | |
| 773 | 0 | |t Gels |g vol. 11, no. 8 (2025), p. 582-610 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244038758/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3244038758/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244038758/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |