Dental composite resin: a review of major mechanical properties, measurements and its influencing factors

Suryawanshi, Abhijeet

doi:10.1002/mawe.202100326

Cited by 7 publications

(10 citation statements)

References 104 publications

(95 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The latter could lead to early attachment debonding. In addition, the repeated insertion and removal forces of the aligners in service dictated selection of materials that demonstrate high levels of strength and rigidity, without excessive deformation and wear, as applies to highly filled materials [23,24]. Finally, BPA-free composites were used in accordance with the current concepts on the toxicological aspect of resin restoratives [4].…”

Section: Discussionmentioning

confidence: 99%

Degree of cure of orthodontic composite attachments underneath aligners

Lasance,

Koletsi,

Eliades

et al. 2023

European J Oral Sciences

View full text Add to dashboard Cite

The aim of this study was to assess the percentage degree of cure (DC%) of 2‐mm‐thick resin composite attachments used for aligner treatment. Three types of aligner – two thermoformed aligners (Clear Aligner [CLA], polyethylene terephthalate glycol modified; and Invisalign [INV], polyester urethane) and a three‐dimensional‐printed aligner (Graphy TC‐85DAC [GRP], an acrylate‐methacrylate copolymer) – were selected, along with two universal resin composites (3M Filtek Universal [FTU] and Charisma Topaz ONE [CTO]). Samples of each composite were placed under each aligner, and the degree of cure of each composite was evaluated on the top (facing the aligner) and the bottom (facing the substrate) attachment surfaces after curing. Five specimens were used per combination of aligner and composite, and an additional group of composites irradiated without aligners served as the control. The DC% measurements were performed using attenuated total reflection Fourier transform infrared (ATR‐FTIR) spectroscopy. The DC% across the aligners were (median values) 33.8%–44.8% for CLA, 33.6%–40.8% for INV, 32.8%–40.6% for GRP, and 40.0%–51.7% for the control group. The DC% values of the attachments cured under any aligner were significantly lower than that of the corresponding control, with the values recorded on the top surfaces being 6% higher than those on the bottom surfaces after adjusting for aligner group and composite type.

show abstract

Section: Discussionmentioning

confidence: 99%

Degree of cure of orthodontic composite attachments underneath aligners

Lasance,

Koletsi,

Eliades

et al. 2023

European J Oral Sciences

View full text Add to dashboard Cite

show abstract

“…The accuracy of the model can be assessed by several statistical parameters, such as absolute mean absolute error, root mean square error, regression coefficient, and average absolute deviation percentage. These parameters are defined by equations (3)(4)(5)(6).…”

Section: Performance Analysis Of Machine Learning Modelsmentioning

confidence: 99%

“…Mechanical properties of dental composites are greatly affected by filler specification, surface roughness, surface treatment, surrounding pH environment, and its method of curing. While developing a new dental composite material it is required to evaluate the different factors which affect the mechanical properties [3].…”

Section: Introductionmentioning

confidence: 99%

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

Suryawanshi,

Behera

2023

Materialwissenschaft Werkst

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Because of their favourable physical and mechanical properties, as well as their ability to restore the biomechanical behaviour of teeth, direct restorations with composite resins are often recommended in most cases. 1 Dental composite testing was previously exclusively modelled on different software in dry conditions. The activity of saliva and other liquids in the mouth, however, changes the conditions for dental composite wear.…”

Section: Introductionmentioning

confidence: 99%

“…More details of the experiment have been provided in the research article published by the author. 1…”

Section: Introductionmentioning

confidence: 99%

Investigation of stress and tribological properties of dental composite materials using FEM

Gupta

Mohan

Jude

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part J:

View full text Add to dashboard Cite

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.

show abstract

Dental composite resin: a review of major mechanical properties, measurements and its influencing factors

Cited by 7 publications

References 104 publications

Degree of cure of orthodontic composite attachments underneath aligners

Degree of cure of orthodontic composite attachments underneath aligners

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

Investigation of stress and tribological properties of dental composite materials using FEM

Contact Info

Product

Resources

About