Predicting Wear Rate and Friction Coefficient of Li<sub>2</sub>Si<sub>2</sub>O<sub>5</sub> Dental Ceramic Using Optimized Artificial Neural Networks

Predicting Wear Rate and Friction Coefficient of Li<sub>2</sub>Si<sub>2</sub>O<sub>5</sub> Dental Ceramic Using Optimized Artificial Neural Networks

The tribological properties of dental materials, such as wear and friction, are crucial for ensuring their long-term reliability and performance. Traditional experimental approaches, while accurate, are often resource intensive and time consuming, prompting a need for efficient computational methods...

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Bibliographic Details
Main Authors:	Marko Pantić, Saša Jovanović, Aleksandar Djordjevic, Suzana Petrović Savić, Milan Radenković, Živče Šarkoćević
Format:	Article
Language:	English
Published:	MDPI AG 2025-02-01
Series:	Applied Sciences
Subjects:	prediction models computer neural network dental wear friction lithium disilicate
Online Access:	https://www.mdpi.com/2076-3417/15/4/1789
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