Prediction of abrasive wears behavior of dental composites using an artificial neural network

Suryawanshi, Abhijeet; Behera, Niranjana

doi:10.1080/10255842.2022.2085509

Cited by 6 publications

(7 citation statements)

References 40 publications

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“…SSE performance can calculate the error rate based on the data pattern formed [22]. Not only that, but SSE is also able to provide stability in analyzing patterns in classifying [23]. In addition to SSE, the PC method also plays an important role in measuring the performance of analytical indicators [24].…”

Section: Introductionmentioning

confidence: 99%

Determination of children's nutritional status with machine learning classification analysis approach

Yanto,

Hadi,

Arlis

2024

IJEECS

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<span>Malnutrition is a problem that is often faced by every country around the world. Various facts show that malnutrition is of particular concern to many researchers. To can overcome this problem, every effort has been made such as developing analytical models in identification, classification, and prediction. This study aims to determine the nutritional status of children using the machine learning (ML) classification analysis approach. The methods used in the ML analysis process consist of cluster K-Means, artificial neural network (ANN), sum square error (SSE), pearson correlation (PC), and decision tree (DT). The dataset for this study uses data on child nutrition cases that occurred in the previous and was sourced from the provincial general hospital (RSUP) M. Djamil, Padang, West Sumatera. Based on the research presented, ML performance in the nutritional status classification analysis gave maximum results. These results are reported based on the level of precision with an accuracy of 99.23%. The results of the analysis can also present a knowledge-based nutritional status classification. This research can contribute to and update the analytical model in determining nutritional status. The results of this study can also provide benefits in handling nutritional status problems that occur in children.</span>

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Section: Introductionmentioning

confidence: 99%

Determination of children's nutritional status with machine learning classification analysis approach

Yanto,

Hadi,

Arlis

2024

IJEECS

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“…Shebani and Iwnicki 27 applied ANN to railway wheels and predicted its wear when operated in different conditions. Suryawanshi and Behera 28 applied ANN methodology to dental implant and investigated their wear performance. Agbeleye et al 29 investigated the effect of heat treatment and clay reinforcements on the wear behaviour of Aluminium composites.…”

Section: Introductionmentioning

confidence: 99%

Dry-sliding wear properties of 3D printed PETG/SCF/OMMT nanocomposites: Experimentation and model predictions using artificial neural network

Mahesh,

George,

Mahesh

et al. 2023

Journal of Reinforced Plastics and Composites

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In this article, an attempt has been made to experimentally investigate the synergistic effect of organically modified montmorillonite (OMMT) nanoclay and short carbon fibers (SCFs) on the tribological behaviour of additively manufactured Polyethylene Terephthalate Glycol (PETG) based nanocomposites. The tribo-specimens are 3D printed using fused deposition modelling (FDM). The tribological properties, that is, specific wear rate (SWR) and coefficient of friction (CoF) of various PETG/SCF/OMMT nanocomposites were assessed by performing dry-sliding wear test. In addition, an artificial neural network (ANN) methodology is proposed to accurately predict the wear performance of PETG nanocomposites. The ANN model is trained using the datasets obtained from the experimentation. For the training of the ANN model, the Levenberg–Marquardt optimisation algorithm with 10 neurons along with a tangent sigmoid activation function is utilised. Additional experimentation was performed for arbitrary load and sliding velocity which were not used for training the ANN model and the results were compared to assess the predictive capability of the ANN model on unseen data. The proposed ANN methodology predicted the SWR and CoF with agreeable accuracy. It is believed that by adopting the proposed ANN methodology, the experimentation costs and time can be significantly reduced without compromising on the accuracy of the results.

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“…With the use of the current set of observations, artificial intelligence models can forecast future events [8,9]. Recently, an artificial neural network model has been developed to predict the wear of dental composites [10]. There is a key distinction between machine learning, deep learning, and artificial intelligence, Figure 1.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning techniques investigated dental implant success prediction. The following techniques were tested: nearest neighbors with structural risk minimization, nearest neighbors with neural networks, support vector machines, and k‐nearest neighbors [27, 28]. Four machine‐learning methods have been developed to identify when a patient could require dental implants [29].…”

Section: Introductionmentioning

confidence: 99%

Prediction of mechanical properties of dental composite materials using machine learning algorithms

Suryawanshi,

Behera

2023

Materialwissenschaft Werkst

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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.

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Prediction of abrasive wears behavior of dental composites using an artificial neural network

Cited by 6 publications

References 40 publications

Determination of children's nutritional status with machine learning classification analysis approach

Determination of children's nutritional status with machine learning classification analysis approach

Dry-sliding wear properties of 3D printed PETG/SCF/OMMT nanocomposites: Experimentation and model predictions using artificial neural network

Prediction of mechanical properties of dental composite materials using machine learning algorithms

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