Application of RBF neural network in prediction of particle damping parameters from experimental data
01 Apr 2017-Journal of Vibration and Control (SAGE PublicationsSage UK: London, England)-Vol. 23, Iss: 6, pp 1077546315587147
TL;DR: In this article, a radial basis function (RBF) neural network was used to predict the modal damping ratio of a particle damping system using system input parameters such as particle size, particle density, packing ratio, and their effect at different modes of vibration.
Abstract: Particle damping is one of the recent passive damping methods and its relevance in space structural applications is increasing. This paper presents the novel application of a radial basis function (RBF) neural network to accurately predict the modal damping ratio of a particle damping system using system input parameters such as particle size, particle density, packing ratio, and their effect at different modes of vibration. The prediction of particle damping using the RBF neural network is studied in comparison with the back propagation neural (BPN) network on an aluminum alloy beam structure with extensive experimental tests. The prediction accuracy of the RBF neural network is significant with 9.83% error compared to 12.22% obtained by the BPN network for a best case. Limited experiments were also carried out on a mild steel beam to study and compare the trends predicted in earlier studies. The relationships obtained by the proposed method readily provide useful guidelines in the design of particle dam...
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TL;DR: In this article, the use of particle damper capsule on a Printed Circuit Board (PCB) and the development of Radial Basis Function neural network to accurately predict the acceleration response is presented.
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01 Jan 2003
TL;DR: In this paper, the feasibility of using particle damping technology on packaging equipment in semiconductor industries, such as a lightly damped bond arm in a die bonding machine, is investigated by way of attaching an enclosure filled with granular materials to appropriate locations on the bond arm for suppressing residual vibration during positioning.
Abstract: Particle damping has been found to be a promising passive vibration suppression technique in aerospace applications. This paper investigates the feasibility of using particle damping technology on packaging equipment in semiconductor industries, such as a lightly damped bond arm in a die bonding machine. It is implemented by way of attaching an enclosure filled with granular materials to appropriate locations on the bond arm for suppressing residual vibration during positioning. In addition, several system parameters are studied experimentally, including granule size, packing ratio, enclosure material, structural form of enclosure, and enclosure location. It has been shown that the vibration of the bond arm is dramatically suppressed by a small quantity of particles.
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References
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16 Jul 1998
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Abstract: From the Publisher:
This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks. Written in a concise and fluid manner, by a foremost engineering textbook author, to make the material more accessible, this book is ideal for professional engineers and graduate students entering this exciting field. Computer experiments, problems, worked examples, a bibliography, photographs, and illustrations reinforce key concepts.
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"Application of RBF neural network i..." refers background in this paper
...…structural vibration energy for a given set of input system parameters with minimum MSE, relative percent deviation (RPD), and with maximum coefficient of determination (R2Þ Further details on different BPN architectures, training algorithms, and termination criteria can be found in Haykin (1998)....
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Journal Article•
TL;DR: The distinct element method as mentioned in this paper is a numerical model capable of describing the mechanical behavior of assemblies of discs and spheres and is based on the use of an explicit numerical scheme in which the interaction of the particles is monitored contact by contact and the motion of the objects modelled particle by particle.
Abstract: The distinct element method is a numerical model capable of describing the mechanical behaviour of assemblies of discs and spheres. The method is based on the use of an explicit numerical scheme in which the interaction of the particles is monitored contact by contact and the motion of the particles modelled particle by particle. The main features of the distinct element method are described. The method is validated by comparing force vector plots obtained from the computer program BALL with the corresponding plots obtained from a photoelastic analysis. The photoelastic analysis used for the comparison is the one applied to an assembly of discs by De Josselin de Jong and Verruijt (1969). The force vector diagrams obtained numerically closely resemble those obtained photoelastically. It is concluded from this comparison that the distinct element method and the program BALL are valid tools for research into the behaviour of granular assemblies. La methode des elements distincts est un modele numerique capab...
12,554 citations
"Application of RBF neural network i..." refers methods in this paper
...Cundall and Strack (1979) pioneered the DEM technique for predicting the behavior of particle damping....
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TL;DR: The distinct element method as mentioned in this paper is a numerical model capable of describing the mechanical behavior of assemblies of discs and spheres and is based on the use of an explicit numerical scheme in which the interaction of the particles is monitored contact by contact and the motion of the objects modelled particle by particle.
Abstract: The distinct element method is a numerical model capable of describing the mechanical behaviour of assemblies of discs and spheres. The method is based on the use of an explicit numerical scheme in which the interaction of the particles is monitored contact by contact and the motion of the particles modelled particle by particle. The main features of the distinct element method are described. The method is validated by comparing force vector plots obtained from the computer program BALL with the corresponding plots obtained from a photoelastic analysis. The photoelastic analysis used for the comparison is the one applied to an assembly of discs by De Josselin de Jong and Verruijt (1969). The force vector diagrams obtained numerically closely resemble those obtained photoelastically. It is concluded from this comparison that the distinct element method and the program BALL are valid tools for research into the behaviour of granular assemblies. La methode des elements distincts est un modele numerique capab...
12,472 citations
TL;DR: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks and is found to be much more efficient than either of the other techniques when the network contains no more than a few hundred weights.
Abstract: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks. The algorithm is tested on several function approximation problems, and is compared with a conjugate gradient algorithm and a variable learning rate algorithm. It is found that the Marquardt algorithm is much more efficient than either of the other techniques when the network contains no more than a few hundred weights. >
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"Application of RBF neural network i..." refers methods in this paper
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"Application of RBF neural network i..." refers methods in this paper
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