Chen, Hongyi;
Liu, Yuanchang;
Balabani, Stavroula;
Hirayama, Ryuji;
Huang, Jie;
(2023)
Machine Learning in Predicting Printable Biomaterial Formulations for Direct Ink Writing.
Research
, 6
, Article 0197. 10.34133/research.0197.
Preview |
PDF
research.0197.pdf - Published Version Download (2MB) | Preview |
Abstract
Three-dimensional (3D) printing is emerging as a transformative technology for biomedical engineering. The 3D printed product can be patient-specific by allowing customizability and direct control of the architecture. The trial-and-error approach currently used for developing the composition of printable inks is time- and resource-consuming due to the increasing number of variables requiring expert knowledge. Artificial intelligence has the potential to reshape the ink development process by forming a predictive model for printability from experimental data. In this paper, we constructed machine learning (ML) algorithms including decision tree, random forest (RF), and deep learning (DL) to predict the printability of biomaterials. A total of 210 formulations including 16 different bioactive and smart materials and 4 solvents were 3D printed, and their printability was assessed. All ML methods were able to learn and predict the printability of a variety of inks based on their biomaterial formulations. In particular, the RF algorithm has achieved the highest accuracy (88.1%), precision (90.6%), and F1 score (87.0%), indicating the best overall performance out of the 3 algorithms, while DL has the highest recall (87.3%). Furthermore, the ML algorithms have predicted the printability window of biomaterials to guide the ink development. The printability map generated with DL has finer granularity than other algorithms. ML has proven to be an effective and novel strategy for developing biomaterial formulations with desired 3D printability for biomedical engineering applications.
| Type: | Article |
|---|---|
| Title: | Machine Learning in Predicting Printable Biomaterial Formulations for Direct Ink Writing |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.34133/research.0197 |
| Publisher version: | https://doi.org/10.34133/research.0197 |
| Language: | English |
| Additional information: | © 2023 Hongyi Chen et al. Exclusive licensee Science and Technology Review Publishing House. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0(https://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10173807 |
Archive Staff Only
 |
View Item |
