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The rapid modeling technique developed in this study addresses on image-based designing technique to create a patient-specific cast.
The good agreement achieved through comparison of MC simulation and experimental results supports the validity of the modeling technique developed.
The objective of this paper is to describe the newly developed EHT process, to report the results of early proof-of-concept experiments, and to provide an explanation for the observed results through the use of a numerical modeling technique developed as a part of this work.
The estimation of the Laplacian distribution parameter α is based on the online correlation noise modeling technique developed by Brites and Pereira [17]: parameter α is estimated for each coefficient of each DCT band.
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A full stochastic multi-scale modeling technique is developed to predict Young's modulus of nanoclay reinforced polymers.
The proposed multiscale modeling technique was developed in the ANSYS (version 13) finite element software environment (ANSYS Inc., Canonsburg, PA, USA) in conjunction with the virtual crack closure technique (VCCT).
To investigate the effect of grouting on the response of proposed reinforcement details a numerical modeling technique was developed based on the infilled-frame concept and calibrated with the experimental behavior of considered walls.
In this article, a multiscale modeling technique is developed to determine the effective elastic moduli of CNT-reinforced epoxy composites containing either well-dispersed or agglomerated carbon nanotubes (CNTs).
The modeling technique is developed based on a reasoning Boolean operation algorithm which consists of merging and extracting operation to construct composite unit cells at the heterogeneous material level.
The objective of this study is to develop a rapid and intelligent modeling technique for developing patient-specific and hygienic orthopedic casts produced by 3D printing technologies.
This paper aims to illustrate the variability of these parameters for a single mid latitude station and demonstrate the ability of the Neural Network (NN) modeling technique for developing a predictive model for these parameters.
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