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.

The application of 3D printing is today one of the technologies that are applied due to its practical application and speed of production of parts. 3D printing is a representative of the concept of rapid prototyping, due to the ease of production of prototypes. In addition to the simplicity of production of parts, certain problems may arise related to the adjustment of the temperature of the extruder and the stand on which the model is printed. Temperatures and the adjustment of temperatures themselves may depend on the material or the part itself that is being produced. Thus, in this paper, an analysis of the temperatures that should be applied depending on the material used from which the part itself is made has been carried out. In addition to the fact that temperature can affect the success of model production or some incidental actions that would prevent printing, temperature can also affect the quality of model production. Thus, in this paper, the influence of temperature on models during printing has been carried out. On the other hand, a review of the temperature limit values that should be applied depending on the material used has been carried out.