The Journal of the Acoustical Society of America

Blast resistance of auxetic and honeycomb sandwich panels: Comparisons and parametric designs

Impact and close-in blast response of auxetic honeycomb-cored sandwich panels: Experimental tests and numerical simulations

In the present study, we applied artificial neural network (ANN) optimized with particle swarm optimization (PSO) for the problem of landslide susceptibility mapping (LSM) prediction. Many studies have revealed that the ANN-based techniques are reliable methods for estimating the LSM. However, most ANN training models facing with major problems such as slow degree of learning system as well as being trapped in their local minima. Optimization algorithms (OA) such as PSO can improve performance results of ANN. Existing applications of PSO model to ANN training have not been used in area of landslide mapping, neither assess the optimal architecture of networks nor the influential factors affecting this problem. Hence, the present study focused on the application of a hybrid PSO-based ANN model (PSO-ANN) to the prediction of landslide susceptibility hazardous mapping. To prepare training and testing datasets for the ANN and PSO-ANN network models, large data collection (i.e., a database consists 168970 training datasets and 42243 testing datasets) were provided from an area of Layleh valley, located in Kermanshah, west of Iran. All the variables of PSO algorithm (e.g., in addition to the network parameter and network weights) were optimized to achieve the most reliable maps of landslide susceptibility. The input dataset includes elevation, slope aspect, slope degree, curvature, soil type, lithology, distance to road, distance to river, distance to fault, land use, stream power index (SPI) and topographic wetness index (TWI), where the output was taken landslide susceptibility value. The predicted results (e.g., from ANN, PSO-ANN) for both of datasets (e.g., training and testing) of the models were assessed based on two statistical indices namely, coefficient of determination (R2) and root-mean-squared error (RMSE). In this study, to evaluate the ability of all methods, color intensity rating (CER) based on the result of above indices was developed. Apart from CER, the total ranking system was also used to rank the obtained statistical indexes. As a result, both models presented good performance, however, according to the introduced ranking system, the PSO-ANN model could perform a better performance compared to ANN. According to R2 and RMSE values of (0.9717 and 0.1040) and (0.99131 and 0.0366) were found for training dataset and values of (0.9733 and 0.111) and (0.9899 and 0.0389) obtained for testing dataset, respectively, for the ANN and PSO-ANN approximation models, it can be resulted that PSO-ANN model showed higher reliability in estimating the LSM compared to the ANN.

Modification of landslide susceptibility mapping using optimized PSO-ANN technique

Different from conventional materials, materials with negative Poisson's ratios expand laterally when stretched longitudinally. Known as ‘auxetic’ materials, the effect means they possess particularly fascinating properties, which have recently attracted considerable attention in the literature. A range of auxetic materials has been discovered, theoretically designed and fabricated. Developments in additive manufacturing (AM) techniques enable fabrication of materials with intricate cellular architectures. This paper outlines recent progress in the development of auxetic materials and structures, and their mechanical properties under quasi-static and dynamic loading are analysed and summarised. Limited experimental studies on 3D printed auxetic materials and structures are given more attention, ahead of extensively finite element (FE) simulations. A special focus is dedicated to their large, plastic deformation behaviour and energy absorption performance, which should be stressed in their engineering applications; no review paper has yet been found regarding this. Finally, this paper provides an overview of current study limitations, and some future research is envisaged in terms of auxetic materials and structures, nano-auxetics and additive manufacturing.

Large deformation and energy absorption of additively manufactured auxetic materials and structures: A review

Multi-physics modeling of the fabrication and dynamic performance of all-metal auxetic-hexagonal sandwich-structures

Purpose – The purpose of this paper is to discuss the recently developed multi-physics computational model for the conventional Gas Metal Arc Welding (GMAW) joining process that has been upgraded with respect to its predictive capabilities regarding the spatial distribution of the mechanical properties controlling the ballistic limit (i.e. penetration resistance) of the weld. Design/methodology/approach – The original model consists of five modules, each dedicated to handling a specific aspect of the GMAW process, i.e.: electro-dynamics of the welding-gun; radiation-/convection-controlled heat transfer from the electric arc to the workpiece and mass transfer from the filler-metal consumable electrode to the weld; prediction of the temporal evolution and the spatial distribution of thermal and mechanical fields within the weld region during the GMAW joining process; the resulting temporal evolution and spatial distribution of the material microstructure throughout the weld region; and spatial distribution ...

Modeling of ballistic-failure mechanisms in gas metal arc welds of mil a46100 armor-grade steel

A thermo-mechanical finite element analysis of the friction stir welding (FSW) process is carried out and the evolution of the material state (e.g., temperature, the extent of plastic deformation, etc.) monitored. Subsequently, the finite-element results are used as input to a Monte-Carlo simulation algorithm in order to predict the evolution of the grain microstructure within different weld zones, during the FSW process and the subsequent cooling of the material within the weld to room temperature. To help delineate different weld zones, (a) temperature and deformation fields during the welding process, and during the subsequent cooling, are monitored; and (b) competition between the grain growth (driven by the reduction in the total grain-boundary surface area) and dynamic-recrystallization grain refinement (driven by the replacement of highly deformed material with an effectively “dislocation-free” material) is simulated. The results obtained clearly revealed that different weld zones form as a result of different outcomes of the competition between the grain growth and grain refinement processes.

R. Galgalikar

Papers

Multi-physics modeling of the fabrication and dynamic performance of all-metal auxetic-hexagonal sandwich-structures

Modeling of ballistic-failure mechanisms in gas metal arc welds of mil a46100 armor-grade steel

Prediction of the Grain-Microstructure Evolution Within a Friction Stir Welding (FSW) Joint via the Use of the Monte Carlo Simulation Method

Finite-element Analysis of Horizontal-axis Wind-turbine Gearbox Failure via Tooth-bending Fatigue

Design Optimization of Honeycomb Core Sandwich Panels for Maximum Sound Transmission Loss