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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MDPI AG
2025-02-01
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| author | Marko Pantić Saša Jovanović Aleksandar Djordjevic Suzana Petrović Savić Milan Radenković Živče Šarkoćević |
| author_facet | Marko Pantić Saša Jovanović Aleksandar Djordjevic Suzana Petrović Savić Milan Radenković Živče Šarkoćević |
| author_sort | Marko Pantić |
| collection | DOAJ |
| description | 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. This study explores the application of artificial neural networks (ANNs) to predict the tribological behavior of dental ceramic lithium disilicate (IPS e.max Cad). A genetic algorithm (GA) was used to optimize the ANN’s hyperparameters, improving its ability to model complex, nonlinear relationships between input variables, including normal load and velocity and output properties such as wear rate and friction coefficients. By integrating experimental data with an ANN, this study identifies key factors influencing tribological performance, reducing the dependency on extensive experimental testing. The results demonstrate that the optimized ANN model accurately predicts tribological behavior, offering a robust framework for material optimization. These findings emphasize the potential of combining ANNs and GAs to enhance the understanding and design of dental materials, accelerating innovation while addressing the challenges of traditional evaluation methods. This research underscores the transformative role of advanced computational approaches in tribology and material science. |
| format | Article |
| id | doaj-art-4a128395932b44ffa641e37ec969ecdd |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-4a128395932b44ffa641e37ec969ecdd2025-08-20T02:01:20ZengMDPI AGApplied Sciences2076-34172025-02-01154178910.3390/app15041789Predicting Wear Rate and Friction Coefficient of Li<sub>2</sub>Si<sub>2</sub>O<sub>5</sub> Dental Ceramic Using Optimized Artificial Neural NetworksMarko Pantić0Saša Jovanović1Aleksandar Djordjevic2Suzana Petrović Savić3Milan Radenković4Živče Šarkoćević5Department of Production Engineering, Faculty of Technical Sciences, University of Priština in Kosovska Mitrovica, 38220 Kosovska Mitrovica, SerbiaFaculty of Engineering, University of Kragujevac, 34000 Kragujevac, SerbiaFaculty of Engineering, University of Kragujevac, 34000 Kragujevac, SerbiaFaculty of Engineering, University of Kragujevac, 34000 Kragujevac, SerbiaDepartment of Production Engineering, Faculty of Technical Sciences, University of Priština in Kosovska Mitrovica, 38220 Kosovska Mitrovica, SerbiaDepartment of Production Engineering, Faculty of Technical Sciences, University of Priština in Kosovska Mitrovica, 38220 Kosovska Mitrovica, SerbiaThe 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. This study explores the application of artificial neural networks (ANNs) to predict the tribological behavior of dental ceramic lithium disilicate (IPS e.max Cad). A genetic algorithm (GA) was used to optimize the ANN’s hyperparameters, improving its ability to model complex, nonlinear relationships between input variables, including normal load and velocity and output properties such as wear rate and friction coefficients. By integrating experimental data with an ANN, this study identifies key factors influencing tribological performance, reducing the dependency on extensive experimental testing. The results demonstrate that the optimized ANN model accurately predicts tribological behavior, offering a robust framework for material optimization. These findings emphasize the potential of combining ANNs and GAs to enhance the understanding and design of dental materials, accelerating innovation while addressing the challenges of traditional evaluation methods. This research underscores the transformative role of advanced computational approaches in tribology and material science.https://www.mdpi.com/2076-3417/15/4/1789prediction modelscomputer neural networkdental wearfrictionlithium disilicate |
| spellingShingle | Marko Pantić Saša Jovanović Aleksandar Djordjevic Suzana Petrović Savić Milan Radenković Živče Šarkoćević 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 Applied Sciences prediction models computer neural network dental wear friction lithium disilicate |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | prediction models computer neural network dental wear friction lithium disilicate |
| url | https://www.mdpi.com/2076-3417/15/4/1789 |
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