Correlating cutting efficiency and debris retention of endodontic files to their design features using AutoCAD measurements

Kataya, Mohamed; Ibrahim, S.; Eid, Ghada El-Helay; El-Mahallawi, I.

doi:10.1016/j.engfailanal.2011.03.030

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…In addition, signs of twisting deformation were also detected along the working blades of TF (Fig. 4) resembling those winding deformation found in RNEF after usage [14].…”

Section: Discussionsupporting

confidence: 62%

“…Since Alexandrou, et al (2006) reported the presence of voids and inclusions in ground sections of Mani NRT RNEF [10], scanning electron microscopic study of ground sections of other systems of RNEF for voids and inclusions [11] has remained scanty. Reports on the detection of surface flaws on RNEF were likewise limited to only very few systems [10][11][12][13][14]. It is the purpose of this study to examine some common brands of RNEF with scanning electron microscope (SEM), so as to provide evidence for the existence of surface flaws, internal voids and inclusions.…”

Section: Introductionmentioning

confidence: 99%

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Scanning Electron Microscopic Study of Rotary Nickel-Titanium Endodontic File (RNEF)

Wu¹,

Chung²

2013

AMR

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This study examined RNEF with scanning electron microscope (SEM), for surface flaws, internal voids and inclusions. Six commercial brands of 4% taper RNEF: PF (#25, 25mm, Dentsply Tulsa Dental Specialties, Tulsa, OK), GTX (#20, 25mm, Dentsply Tulsa Dental Specialties, Tulsa, OK), NRT (#25, 25mm, MANI Inc., Tochigi, Japan), FKG (#25, 25mm, FKG, Dentaire), K3 (#25, 25mm) and TF (Twisted File #25, 27mm) (Sybron Kerr, Orange, CA) were examined. Full length and 4mm segment of each RNEF brand was mounted in Al plate for surface morphology and conductive epoxy resin for cross-sectional imaging with SEM (JEOL JSM-820) respectively. EDX spectrum was collected from specimens with EDX spectrometer coupled to SEM. EDX software was used for quantitative analysis. This study demonstrated the existence of surface flaws as machining marks, notches, crevices, fissures and pits. Internal voids of micro-to sub-micrometer magnitude were observed. Ti-rich inclusions were found dispersed in the equiatomic to near-equiatomic NiTi substrate of RNEF. Surface flaws, internal voids and inclusions were common findings on RNEF studied. Their effects on the corrosion and fatigue resistance of RNEF deserve further investigation.

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“…In addition, signs of twisting deformation were also detected along the working blades of TF (Fig. 4) resembling those winding deformation found in RNEF after usage [14].…”

Section: Discussionsupporting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Scanning Electron Microscopic Study of Rotary Nickel-Titanium Endodontic File (RNEF)

Wu¹,

Chung²

2013

AMR

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“…The rotary instrument is comparably given a higher torque than the manual preparation. 11 Kataya et al 12 compared the endodontic instrument’s debris retention and cutting efficiency through the computer image analysis program. Afterward, it concluded that electro-polishing could enhance and improve the instrument’s wear resistance.…”

Section: Introductionmentioning

confidence: 99%

Health prediction of reciprocating endodontic instrument based on the machine learning and exponential degradation models

Thakur,

Kankar,

Parey

et al. 2023

Proc Inst Mech Eng H

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This study proposes an intelligent health prediction and fault prognosis of the endodontic file during the root canal treatment. Root canal treatment is the procedure of disinfecting the infected pulp through the canal with the help of an endodontic instrument. Force signals are acquired with the help of a dynamometer during the canal preparation, and statistical features are extracted. The extracted features are selected through the window-wise feature extraction process. Characteristic features for endodontic file prognostics include time-domain features of the signals are evaluated. The extracted feature has inappropriate information, that is, noise between the signals; hence the smoothing of the feature is required at this stage to observe a trend in the signals. Based on the smoothing feature and post-processing of the feature, defined the health index to calculate the health condition of the endodontic instruments. A machine learning algorithm and exponential degradation model are used to predict the health of the endodontic instrument during the root canal treatment. This model is used to forecast the degradation of the endodontic file so that actions can be taken before actual failures happen. The proposed methodology can analyze the failures and micro-crack initiation of the endodontic instruments. Endodontics practitioners can use the machine learning models as well as an exponential model for estimating the health condition of the endodontic instrument. This study may help the clinician to progress the efficiency of the root canal treatment and the competence of the endodontic instruments.

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Correlating cutting efficiency and debris retention of endodontic files to their design features using AutoCAD measurements

Cited by 2 publications

References 11 publications

Scanning Electron Microscopic Study of Rotary Nickel-Titanium Endodontic File (RNEF)

Scanning Electron Microscopic Study of Rotary Nickel-Titanium Endodontic File (RNEF)

Health prediction of reciprocating endodontic instrument based on the machine learning and exponential degradation models

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