Theoretical and FE analysis of ultrasonic welding of aluminum alloy 3003

Ultrasonic welding (consolidation) process is a rapid manufacturing process that is used to join thin layers of metal at low temperature and low energy consumption. Experimental results have shown that ultrasonic welding is a combination of both surface (friction) and volume (plasticity) softening effects. In the presented work, an attempt has been made to simulate the ultrasonic welding of metals by taking into account these effects (surface and volume). A phenomenological material model has been proposed, which incorporates these two effects (i.e., surface and volume). The thermal softening due to friction and ultrasonic (acoustic) softening has been included in the proposed material model. For surface effects, a friction law with variable coefficient of friction that is dependent on contact pressure, slip, temperature, and number of cycles has been derived from experimental friction tests. The results of the thermomechanical analyses of ultrasonic welding of aluminum alloy have been presented. The goal of this work is to study the effects of ultrasonic welding process parameters, such as applied load, amplitude of ultrasonic oscillation, and velocity of welding sonotrode on the friction work at the weld interface. The change in the friction work at the weld interface has been explained on the basis of softening (thermal and acoustic) of the specimen during the ultrasonic welding process. In the end, a comparison between experimental and simulated results has been presented, showing a good agreement. Copyright © 2009 by ASME.

Local materials for building houses: laterite valorization in Africa

This paper presents the preliminary results of geological and geomechanical studies on the laterite stone exploited at Dano quarry in Burkina Faso. The field work described the geological structure of quarry sites and their environment to determine the rocks alteration and the links between the bedrock and lateritic material. Physic-mechanical properties have been studied for assessing the potentiality of this material for lightweight housing, to be completed with thermal and environmental considerations. Some social and economic evaluations are in progress in order to foster its utilization under local conditions.

Modelling and characterization of ultrasonic consolidation process of Aluminium alloys

Ultrasonic consolidation process is a rapid manufacturing process used to join thin layers of metal at low temperatures and low energy consumption. In this work, finite element method has been used to simulate the ultrasonic consolidation of Aluminium alloys 6061 (AA-6061) and 3003 (AA-3003). A thermomechanical material model has been developed in the framework of continuum cyclic plasticity theory which takes into account both volume (acoustic softening) and surface (thermal softening due to friction) effects. A friction model based on experimental studies has been developed, which takes into account the dependence of coefficient of friction upon contact pressure, amount of slip, temperature and number of cycles. Using the developed material and friction model ultrasonic consolidation (UC) process has been simulated for various combinations of process parameters involved. Experimental observations are explained on the basis of the results obtained in the present study. The current research provides the opportunity to explain the differences of the behaviour of AA-6061 and AA-3003 during the ultrasonic consolidation process. Finally, trends of the experimentally measured fracture energies of the bonded specimen are compared to the predicted friction work at the weld interface resulted from the simulation at similar process condition. Similarity of the trends indicates the validity of the developed model in its predictive capability of the process. © 2008 Materials Research Society.

Study of localized damage in composite laminates using micro-macro approach

A robust multiscale scheme referred to as micro–macro method has been developed for the prediction of localized damage in fiber reinforced composites and implemented in a finite element framework. The micro–macro method is based on the idea of partial homogenization of a structure. In this method, the microstructural details are included in a small region of interest in the structure and the rest is modeled as a homogeneous continuum. The solution to the microstructural fields is then obtained on solving the two different domains, simultaneously. This method accurately predicts local stress fields in stress concentration regions and is computationally efficient as compared with the solution of a full scale microstructural model. This scheme has been applied to obtain localized damage at high and low stress zones of a V-notched rail shear specimen. The prominent damage mechanisms under shear loading, namely, matrix cracking and interfacial debonding, have been modeled using Mohr–Coulomb plasticity and traction separation law, respectively. The average stress at the notch has been found to be 44% higher than the average stresses away from the notch for a 90 N shear load. This stress rise is a direct outcome of the geometry of the notch.

DFIG Machine Design for Maximizing Power Output Based on Surrogate Optimization Algorithm

This paper presents a surrogate-model based optimization of a doubly-fed induction generator (DFIG) machine winding design for maximizing power yield. Based on site-specific wind profile data and the machine’s previous operational performance, the DFIG’s stator and rotor windings are optimized to match the maximum efficiency with operating conditions for rewinding purposes. The particle swarm optimization (PSO)-based surrogate optimization techniques are used in conjunction with the finite element method (FEM) to optimize the machine design utilizing the limited available information for the site-specific wind profile and generator operating conditions. A response surface method in the surrogate model is developed to formulate the design objectives and constraints. Besides, the machine tests and efficiency calculations follow IEEE standard 112-B. Numerical and experimental results validate the effectiveness of the proposed technologies.

Impact of finite element idealisation on the prediction of welded fuselage stiffened panel buckling

Lap joints are widely used in the manufacture of stiffened panels and influence local panel sub-component stability, defining buckling unit dimensions and boundary conditions. Using the Finite Element method it is possible to model joints in great detail and predict panel buckling behaviour with accuracy. However, when modelling large panel structures such detailed analysis becomes computationally expensive. Moreover, the impact of local behaviour on global panel performance may reduce as the scale of the modelled structure increases. Thus this study presents coupled computational and experimental analysis, aimed at developing relationships between modelling fidelity and the size of the modelled structure, when the global static load to cause initial buckling is the required analysis output. Small, medium and large specimens representing welded lap-joined fuselage panel structure are examined. Two element types, shell and solid-shell, are employed to model each specimen, highlighting the impact of idealisation on the prediction of welded stiffened panel initial skin buckling.

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.

An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models

Clean and renewable energy generation and supply has drawn much attention worldwide in recent years, the proton exchange membrane (PEM) fuel cells and solar cells are among the most popular technologies. Accurately modeling the PEM fuel cells as well as solar cells is critical in their applications, and this involves the identification and optimization of model parameters. This is however challenging due to the highly nonlinear and complex nature of the models. In particular for PEM fuel cells, the model has to be optimized under different operation conditions, thus making the solution space extremely complex. In this paper, an improved and simplified teaching-learning based optimization algorithm (STLBO) is proposed to identify and optimize parameters for these two types of cell models. This is achieved by introducing an elite strategy to improve the quality of population and a local search is employed to further enhance the performance of the global best solution. To improve the diversity of the local search a chaotic map is also introduced. Compared with the basic TLBO, the structure of the proposed algorithm is much simplified and the searching ability is significantly enhanced. The performance of the proposed STLBO is firstly tested and verified on two low dimension decomposable problems and twelve large scale benchmark functions, then on the parameter identification of PEM fuel cell as well as solar cell models. Intensive experimental simulations show that the proposed STLBO exhibits excellent performance in terms of the accuracy and speed, in comparison with those reported in the literature.

FEM Analysis to Optimally Design End Mill cutters for Milling of Ti-6Al-4V

This paper presents an FEM analysis conducted for optimally designing end mill cutters through verifying the cutting tool forces and stresses for milling Titanium alloy Ti-6Al-4 V. Initially, the theoretical tool forces are calculated by considering the cutting edge on a cutting tool as the curve of an intersection over a spherical/flat surface based on the model developed by Lee & Altinas [1]. Considering the lowest tool forces the cutting tool parameters are taken and optimal design of end mill is decided for different sizes. Then the 3D CAD models of the end mills are developed and used for Finite Element Method to verify the cutting forces for milling Ti-6Al-4 V. The cutting tool forces, stress, strain concentration (s), tool wear, and temperature of the cutting tool with the different geometric shapes are simulated considering Ti-6Al-4 V as work piece material. Finally, the simulated and theoretical values are compared and the optimal design of cutting tool for different sizes are validated. The present approach considers to improve the quality of machining surface and tool life with effects of the various parameters concerning the oblique cutting process namely axial, radial and tangential forces. Various simulated test cases are presented to highlight the approach on optimally designing end mill cutters.

Semantic feature-based visual attention model for pedestrian detection

Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.