Aim:To use Atomic Force Microscope and Energy Dispersive X-ray Spectroscopy to evaluate the effect of 5% NaOCl, 17% EDTA and triphala on ProTaper and iRaCe rotary Ni-Ti instruments.Methodology:A total of eight Ni-Ti rotary files, four files each of ProTaper - S2 (Dentsply) and iRaCe - R3 (FKG DENTAIRE) were used. Three out of four files each from ProTaper and iRaCe were immersed in 5% NaOCl, 17% EDTA and Triphala separately for five minutes. The Roughness average (Ra), Root Mean Square (RMS) and Mean Height of Roughness Profile Elements (Rc) of the scanned profiles were then recorded using AFM and the elemental composition was evaluated with EDS. Data were analyzed by Student's t test, One Way ANOVA and Duncan's Multiple Range Test.Results:Topographic irregularities at the nanometric scale were observed for all files. Files immersed in EDTA and NaOCl showed highly significant surface roughness than untreated files.Conclusion:Short-term contact with 17% EDTA and 5% NaOCl can cause significant surface deterioration of ProTaper and iRaCe rotary NiTi files. AFM proves to be a suitable method for evaluating the instrument surface.
a b s t r a c tWavelet method is a recently developed tool in applied mathematics. Investigation of various wavelet methods, for its capability of analyzing various dynamic phenomena through waves gained more and more attention in engineering research. Starting from 'offering good solution to differential equations' to capturing the nonlinearity in the data distribution, wavelets are used as appropriate tools at various places to provide good mathematical model for scientific phenomena, which are usually modeled through linear or nonlinear differential equations. Review shows that the wavelet method is efficient and powerful in solving wide class of linear and nonlinear reaction-diffusion equations. This review intends to provide the great utility of wavelets to science and engineering problems which owes its origin to 1919. Also, future scope and directions involved in developing wavelet algorithm for solving reaction-diffusion equations are addressed.Crown
Artificial intelligent tools like genetic algorithm, artificial neural network (ANN) and fuzzy logic are found to be extremely useful in modeling reliable processes in the field of computer integrated manufacturing (for example, selecting optimal parameters during process planning, design and implementing the adaptive control systems). When knowledge about the relationship among the various parameters of manufacturing are found to be lacking, ANNs are used as process models, because they can handle strong nonlinearities, a large number of parameters and missing information. When the dependencies between parameters become noninvertible, the input and output configurations used in ANN strongly influence the accuracy. However, running of a neural network is found to be time consuming. If genetic algorithm-based ANNs are used to construct models, it can provide more accurate results in less time. This article proposes a genetic algorithm-based ANN model for the turning process in manufacturing Industry. This model is found to be a timesaving model that satisfies all the accuracy requirements.
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