Niranjana Behera

Plenty of dental restorative composite materials are available in the market, which makes it challenging to select a good wear-resistant material. The durability of different dental materials in human mouth depends on tribological characteristics of the materials. Clinical efficacy of the restorative materials cannot be predicted just based on tribological characteristics. Nevertheless, these characteristics still can support the investigation of the consequences of modifications to their composition. Numerous laboratory studies have been used to characterize the tribological properties of the materials, which helped clinicians in selecting the appropriate dental materials. This article reviews the important influential factors while evaluating the tribological properties of dental composite materials using a pin-on-disk tribometer, including surface roughness of the specimen, normal load, type of pin material, pH level, loading cycle, and abrasive medium. The influences of the factors on measurements of tribological characteristics and the rules for selection of restorative materials are also discussed.

Tests specimens were prepared by friction stir welding of two dissimilar metals aluminum and copper. The specimens were subjected to mechanical tests to calculate the ultimate tensile strength, yield strength, percentage elongation, and impact energy. Four different machine learning algorithms (AdaBoost, CatBoost, Gradient Boosting, and XGBoost) were applied for developing the ML models in predicting the performance parameters such as ultimate strength, yield strength, percentage elongation, and impact energy. Pin type, weld speed, rotational speed, and shoulder diameter were considered as the input parameters for the model. Training, testing, and validation were carried out by considering 60%, 20%, and 20% of the available data respectively. In terms of accuracy (lower MAE, lower RMSE, greater R ² value, and lower AAD%), CatBoost model, Gradient Boosting model, and XGBoost model performed better than the AdaBoost model in predicting the ultimate tensile strength, yield strength, percentage elongation, and impact energy. Compared to other models, AdaBoost model has only few hyperparameters for fine-tuning. During hyperparameters tuning, AdaBoost model showed accuracy only within a narrow range of values of features.

Conventional liquid lubricants prove inadequate for effective lubrication in conditions characterized by high temperatures and high vacuum environments. In such extreme scenarios, powder lubricants emerge as a more viable solution. The present study is to conduct a series of experiments using a reciprocating wear test setup and evaluate the capability of four different machine learning models in analysing the tribological attributes of metals when lubricated with three distinct powder types: zirconium dioxide, copper oxide, and molybdenum disulfide, specifically under conditions of elevated contact pressures and dry environments. The experiments were systematically carried out encompassing a range of load and temperature combinations. Four different machine learning models (MLP, KNN, extreme gradient boosting and light gradient‐boosting machine) were used for predicting the coefficient of friction of metals lubricated with different powders. Extreme gradient boosting machine learning model gives better result than the other models with mean absolute error, root mean squared error, R²value and average absolute deviation percentage of 0.0215, 0.0278, 0.9962 and respectively.

It is difficult to model the pressure drop that occurs in fluidized dense phase conveying (FDP) of powders because the flow involves several interactions among the solid, gas, and pipe wall. These interactions are challenging to include in a model. Pressure drop is influenced by geometrical, material, and flow properties. When used with different pipeline designs that have different pipeline lengths or diameters, the current models exhibit considerable inaccuracy. The current work explores how machine learning (ML) algorithms can estimate the pressure drop in the FDP conveying of particles. The network was trained using experimental data from pneumatic conveying, and it subsequently used that information to predict pressure drops. For estimating the pressure drop, four distinct ML algorithms-AdaBoost, CatBoost, gradient boosting, and random forest-were selected. AdaBoost, CatBoost, gradient boosting, and random forest models predicted the data of pressure drop with MAE of 20.72, 4.06, 4.68, and 3.0, respectively, for training as well as testing data. The AdaBoost model performed more poorly in predicting the pressure drop than other models considered for the study, with ± 10% error margin while training and evaluating the data and ± 10% error margin in validating the data.

The durability of dental materials, when used in the mouth, is determined by their mechanical qualities. Composite resins are frequently used in dental restorations. Flexural tests and Vickers micro‐hardness tests on selected dental composite materials were performed in a universal testing machine (ASTM D790‐10 standard) and Vickers micro‐hardness tester (ASTM E384‐11e1standard). In this study, four different dental composite material samples are employed. The samples are dipped in a chewing tobacco solution for a few days before being removed and put through the tests. Also in this work, four different machine learning models were tested to see how well they could analyze the mechanical characteristics of dental composite materials when submerged in a chewing tobacco solution. For predicting the mechanical properties of dental composite specimens, four distinct machine‐learning models (extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), random forest, and k‐nearest neighbors (KNN) have been selected. AdaBoost machine learning model yields a coefficient of regression value of 0.9903 in predicting the flexural strength, whereas the XGBoost model gives a coefficient of regression value of 0.9890 in predicting the Vickers hardness distinctly better than the other models.

Modeling of pressure drop in fluidized dense phase conveying (FDP) of powders is a tough work as the flow comprises of various interactions among solid, gas and pipe wall. It is difficult to incorporate these interactions into a model. The pressure drop depends on flow, material and geometrical parameters. The existing models show high error when applied to other pipeline configurations of varying pipeline lengths or diameters. The current study investigates the capability of machine learning (ML) techniques to estimate the drop in pressure in FDP conveying of powders. Pneumatic conveying experimental data were used for training the network and then for predicting the pressure drop. For estimating the pressure drop, four distinct ML algorithms light gradient boosting machine (LighGBM)), multilayer perception (MLP), K-nearerst neighbors (KNN), extreme gradient boosting (XGBoost), and were selected. XGBoost model performed better than other models chosen for the study with ±5% error margin while training and testing the data, and ±10% error margin in validating the data. MLP, XGBoost, KNN, and LightGBM models predicted the data of pressure drop with MAE of 5.05, 1.19, 5.72, and 2.85, respectively, for training as well as testing data. Among the four models considered, the model using XGBoost algorithm performed the best, whereas the model using KNN algorithm performed poorly in predicting the FDP conveying pressure drop.

Since dental materials are worn down over time and eventually need to be replaced. Resin composites are frequently employed as dental restorative materials. By employing the in-vitro test findings of the pin-on-disc tribometer [ASTM G99-04], the goal of this study is to evaluate the capability of three different machine learning (ML) models in analyzing the wear of dental composite materials when immersed in chewable tobacco solution. Four distinct dental composite material samples are used in this investigation, and after being dipped in a chewing tobacco solution for a few days, the samples are taken out and subjected to a wear test. Three different ML models (MLP, KNN, XGBoost) have been chosen for predicting the wear of dental composite specimens. XGBoost ML model yields an R² value of 0.9996 and it performs noticeably better than the other approaches.

Performance of dental materials is influenced by the abrasion and wear qualities of the materials in contact. Despite major advancements in dental materials over the previous few decades, restorative materials, particularly composite resins, continue to wear out. Wear resistance is directly influenced by several composite resin composition and structure characteristics. The focus of this research is to explore the tribological properties of two commercially available dental composite materials using ANSYS software (Tetric N-Ceram and Z350 Dentin Shade). In the experiment, a simple tribo-system that complies with American Society for Testing and Materials (ASTM) G99 is utilised, consisting of a cylinder-shaped pin that can be deformed, rotating around a disc under wet conditions. A comparison of Z350 dentin shade and Tetric n Ceram simulations for the same load and simulation time revealed that the former material had greater wear resistance. Simulation results showed that as the stress on the pin increases, wear for both materials increases, which is supported by experiment data from the pin-on-disc tribometer setup.