The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform
Horst Biermann - One of the best experts on this subject based on the ideXlab platform.
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estimation of the effective properties of Particle reinforced metal matrix composites from microtomographic reconstructions
Acta Materialia, 2006Co-Authors: A Borbely, Peter Kenesei, Horst BiermannAbstract:Abstract A new approach for the estimation of the effective properties of Real Particle-reinforced metal–matrix composites is presented. The methodology relies on statistical functions of the three-dimensional structure of the composites, as obtained from microtomographic investigations, and uses an ergodic principle for the calculation of the overall properties. Based on the symmetrical functional form of the local volume fraction and results of finite element calculations we develop the so-called “mean window technique”, which leads to the remarkable result that the effective linear or nonlinear mechanical properties of a composite can be obtained by averaging randomly selected windows from the Real structure. Such windows should have a size at least equal to the correlation length of the microstructure and the volume fraction of the Particles equal to the mean value of the distribution of the local volume fraction.
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Estimation of elastic properties of Particle reinforced metal-matrix composites based on tomographic images
Advanced Engineering Materials, 2006Co-Authors: Peter Kenesei, Horst Biermann, András BorbélyAbstract:The structure of a Real Particle reinforced metal-matrix composite, Al/Al 2 O 3 , was retrieved by high resolution X-ray tomography. The paper presents a study of the influence of local structure variability on the elastic properties of the composite. The results are in good agreement with theoretical two-point bounds.
Peter Kenesei - One of the best experts on this subject based on the ideXlab platform.
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estimation of the effective properties of Particle reinforced metal matrix composites from microtomographic reconstructions
Acta Materialia, 2006Co-Authors: A Borbely, Peter Kenesei, Horst BiermannAbstract:Abstract A new approach for the estimation of the effective properties of Real Particle-reinforced metal–matrix composites is presented. The methodology relies on statistical functions of the three-dimensional structure of the composites, as obtained from microtomographic investigations, and uses an ergodic principle for the calculation of the overall properties. Based on the symmetrical functional form of the local volume fraction and results of finite element calculations we develop the so-called “mean window technique”, which leads to the remarkable result that the effective linear or nonlinear mechanical properties of a composite can be obtained by averaging randomly selected windows from the Real structure. Such windows should have a size at least equal to the correlation length of the microstructure and the volume fraction of the Particles equal to the mean value of the distribution of the local volume fraction.
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Estimation of elastic properties of Particle reinforced metal-matrix composites based on tomographic images
Advanced Engineering Materials, 2006Co-Authors: Peter Kenesei, Horst Biermann, András BorbélyAbstract:The structure of a Real Particle reinforced metal-matrix composite, Al/Al 2 O 3 , was retrieved by high resolution X-ray tomography. The paper presents a study of the influence of local structure variability on the elastic properties of the composite. The results are in good agreement with theoretical two-point bounds.
A Borbely - One of the best experts on this subject based on the ideXlab platform.
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estimation of the effective properties of Particle reinforced metal matrix composites from microtomographic reconstructions
Acta Materialia, 2006Co-Authors: A Borbely, Peter Kenesei, Horst BiermannAbstract:Abstract A new approach for the estimation of the effective properties of Real Particle-reinforced metal–matrix composites is presented. The methodology relies on statistical functions of the three-dimensional structure of the composites, as obtained from microtomographic investigations, and uses an ergodic principle for the calculation of the overall properties. Based on the symmetrical functional form of the local volume fraction and results of finite element calculations we develop the so-called “mean window technique”, which leads to the remarkable result that the effective linear or nonlinear mechanical properties of a composite can be obtained by averaging randomly selected windows from the Real structure. Such windows should have a size at least equal to the correlation length of the microstructure and the volume fraction of the Particles equal to the mean value of the distribution of the local volume fraction.
Feng Qian - One of the best experts on this subject based on the ideXlab platform.
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Tuning the structure and parameters of a neural network using cooperative binary-Real Particle swarm optimization
Expert Systems with Applications, 2011Co-Authors: Liang Zhao, Feng QianAbstract:Research highlights? The cooperative binary-Real Particle swarm optimization is employed. ? The structure and parameters of neural networks are optimized simultaneously. ? The neural network model with compact structure and better learning ability is obtained. In this paper, a cooperative binary-Real Particle swarm optimization is applied to tune the structure and parameters of a neural network. A neural network with switches of its links, which is used to decide whether there is a link between two neurons or not, is introduced firstly. Thus, the structure of a neural network can be decided by the switches. A cooperative binary-Real Particle swarm optimization algorithm is utilized to find the compact structures and optimal parameters of the proposed neural network. The number of hidden nodes of the neural network is increased from a small number until its learning ability is achieved. The simulation experiments indicate that the proposed approach can obtain better results than the existing approaches in recent literature.
Sinan T Erdogan - One of the best experts on this subject based on the ideXlab platform.
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spherical harmonic based random fields based on Real Particle 3d data improved numerical algorithm and quantitative comparison to Real Particles
Powder Technology, 2011Co-Authors: X Liu, Edward J Garboczi, M Grigoriu, Sinan T ErdoganAbstract:Abstract The shape of Particles often plays an important role in how they are used and in the properties of composite systems in which they are incorporated. When building models of systems that include Real Particles, it is often of interest to generate new, virtual Particles whose 3D shape statistics are based on the 3D shape statistics of a collection of Real Particles. A previous paper showed mathematically how this can be carried out, but only had a small set of Real Particle shape data to use and only made a limited amount of qualitative comparisons to the Real Particle data. The present paper shows how the numerical method used to create virtual Particles has been improved and immensely accelerated, allowing the use of large Particle datasets. Making use of several large Particle shape datasets, the paper confirms that the algorithm creates Particles whose statistical shape properties closely match the Real Particles from which they were generated. Another question that can now be addressed with these larger Particle datasets is: how many Real Particles are enough to be representative of the Particle class from which they were drawn? The types of Particles analyzed include two size ranges of crushed granite-hornblende rocks, silica sand, calcium carbonate powder, and ground granulated blast furnace slag.
