| Texto completo | |
| Autor(es): |
Ramachandran, Remya Ampadi
;
Barao, Valentim A. R.
;
Ozevin, Didem
;
Sukotjo, Cortino
;
Pai, P. Srinivasa
;
Mathew, Mathew
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | TRIBOLOGY INTERNATIONAL; v. 187, p. 13-pg., 2023-06-29. |
| Resumo | |
Early detection and prediction of bio-tribocorrosion can avert unexpected damage that may lead to secondary revision surgery and associated risks of implantable devices. Therefore, this study sought to develop a state-ofthe-art prediction technique leveraging machine learning(ML) models to classify and predict the possibility of mechanical degradation in dental implant materials. Key features considered in the study involving pure titanium and titanium-zirconium (zirconium = 5, 10, and 15 in wt%) alloys include corrosion potential, acoustic emission(AE) absolute energy, hardness, and weight-loss estimates. ML prototype models deployed confirms its suitability in tribocorrosion prediction with an accuracy above 90%. Proposed system can evolve as a continuous structural-health monitoring as well as a reliable predictive modeling technique for dental implant monitoring. (AU) | |
| Processo FAPESP: | 20/05231-4 - Desenvolvimento de um novo hidrogel termosensitivo para liberação controlada de drogas direcionado a prevenção e ao tratamento de inflamações peri-implantares |
| Beneficiário: | Valentim Adelino Ricardo Barão |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |