Sharif Digital Repository

This paper attempts to predict how the microstructural features and mechanical properties of the individual constituents affect the deformation behavior and formability of ferrite-pearlite steels under quasi-static loading at room temperature. For this purpose, finite element simulations using representative volume elements (RVEs) based on the real microstructures were implemented to model the flow behavior of the ferrite-pearlite steels with various microstructural morphologies (non-banded and banded). The homogenized flow curves obtained from the RVEs subjected to periodic boundary conditions together with displacement boundary conditions were validated with the experimental results of the... 

This paper attempts to study the microstructural stress-strain evolution and strain localization in the ferrite-pearlite steel by high-resolution experimental-numerical integrated testing. Ferrite crystal orientations measured by electron backscatter diffraction (EBSD) were mapped onto precise scanning electron microscopy (SEM) micrographs of ferrite and cementite lamellar morphologies. Furthermore, in-situ SEM tensile testing was employed to map strains during the deformation using digital image correlation (DIC) at high spatial resolutions. Finally, spectral solver-based crystal plasticity (CP) simulations loaded by the local SEM-DIC boundary conditions were performed and compared to the... 

This article presents an innovative application of the frequency domain decomposition method based on an acoustic and vibration response. Frequency domain decomposition method has been frequently used for operational modal analysis testing in the last decade to identify modal parameters for in-situ case studies. For these studies, the outputs of the vibration response through accelerometers have been employed in the analysis. In this article, the frequency domain decomposition method is employed, for the first time, to analyze both acoustic and vibration response of the building which is a novel application in building vibration response. As a case study, a cylindrical shaped seven-story... 

The main objective of this study is to predict the deformation behavior, strain localization, and forming limits of ferrite-pearlite steels by incorporating the contributions of the microstructural characteristics and mechanical properties of the underlying microstructure. A realistic microstructure-based micromechanical approach in the framework of the crystal plasticity (CP) model was carried out using the periodic representative volume element (RVE) generated from the scanning electron microscopy (SEM) image. The homogenized stress–strain curve of the realistic RVE was validated with the experimental data with an error of less than 6.71% at large strains. Afterward, the initial... 

Hybrid nanoparticle comprising of nanosilica (NS) and nanodiamond (ND), denoted by ND@NS, was synthesized by a two-step chemistry route and then incorporated into styrene-butadiene rubber (SBR) at low concentrations up to 5 phr by a two-roll mill. Physical hybrids of NS and ND, i.e., ND&NS, with a weight ratio similar to chemical hybrid (80:20 for NS:ND), were also incorporated into SBR. Field emission scanning electron microscopy observations revealed very fine dispersion of nanoparticles in the case of physical hybrids (ND&NS). In contrast, agglomerations and aggregation of nanoparticles were evidenced for ND@NS. Tensile and dynamic mechanical analyses exhibited a higher reinforcing effect... 

Balancing a rotating machine is a vital step towards ensuring it will operate reliably and safely. To achieve this condition monitoring is an important aspect for early faulty detection and it can also result in the improvement of the operational continuity and equipment safety. Imbalance itself, is a common problem in rotating machinery and it can lead to poor or even dangerous vibration levels. And, for example, it is also well known that misalignment can lead to bearing defects. So, in order to overcome these problems many balancing techniques have been developed that are largely based on measured vibration data using accelerometers attached to the faulty device. The three-point method is... 

Current machine learning models achieve super-human performance in many real-world applications. Still, they are susceptible against imperceptible adversarial perturbations. The most effective solution for this problem is adversarial training that trains the model with adversarially perturbed samples instead of original ones. Various methods have been developed over recent years to improve adversarial training such as data augmentation or modifying training attacks. In this work, we examine the same problem from a new data-centric perspective. For this purpose, we first demonstrate that the existing model-based methods can be equivalent to applying smaller perturbation or optimization...