LSTM-based prediction of wear in 3D-printed restorative materials under various methods
This study demonstrates the feasibility of applying LSTM models for early and accurate prediction of wear progression in AM dental materials, offering potential for reducing physical testing duration and enhancing data-driven material evaluation frameworks for clinical applications.
Authors
- Sunyoung Ma (ORCID: https://orcid.org/0000-0003-4722-7766)
- Kai Chun Li
- Alexander Hui Xiang Yang (ORCID: https://orcid.org/0009-0008-8823-7059)
- Anastasiia Grymak (ORCID: https://orcid.org/0000-0002-9643-5038)
Institutions
- Bridge Pharma (United States) (US)
- University of Otago (NZ)
Publication Details
- Journal
- Dental Materials
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1016/j.dental.2025.09.012
- Primary Topic
- Additive Manufacturing and 3D Printing Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00