Application of artificial neural networks to composite ply micromechanics
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Application of artificial neural networks to composite ply micromechanics

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Published by National Aeronautics and Space Administration, Lewis Research Center, For sale by the National Technical Information Service in [Cleveland, Ohio, Springfield, Va .
Written in English


  • Artificial intelligence.,
  • Characterization.,
  • Composite materials.,
  • Micromechanics.,
  • Neural nets.,
  • Parallel processing (Computers)

Book details:

Edition Notes

StatementD.A. Brown, P.L.N. Murthy, and L. Berke.
SeriesNASA technical memorandum -- 104365.
ContributionsMurthy, P. L. N., Berke, Laszlo., Lewis Research Center.
The Physical Object
Pagination1 v.
ID Numbers
Open LibraryOL16124025M

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Artificial Neural Networks for Engineering Applications presents current trends for the solution of complex engineering problems that cannot be solved through conventional methods. The proposed methodologies can be applied to modeling, pattern recognition, classification, forecasting, estimation, and . Application of artificial neural networks to composite ply micromechanics. The primary goal is to demonstrate the applicability of artificial neural networks to composite material characterization. As a test case, a neural network was trained to accurately predict composite hygral, thermal, and mechanical properties when provided with Author: D. A. Brown, P. L. N. Murthy and L. Berke. The application of artificial neural networks in civil engineering is gaining momentum. Most of the applications, however, use a feedforward network with a back-propagation algorithm. To preserve high precision of the microequipment, it is necessary to use adaptive algorithms of micropiece production. Algorithms based on contact sensors were proved and showed good results [2]. The neural-network-based vision system could provide much more extensive possibilities to improve the manufacturing by: 1.

2. Artificial neural networks What are artificial neural networks? Artificial neural networks are computational systems that simulate the microstructure (neurons) of a biological nervous system. The most basic components of ANNs are modeled after the structure of the brain, and therefore even the terminology is borrowed from by: Artificial Neural Networks for Beginners Carlos Gershenson [email protected] 1. Introduction The scope of this teaching package is to make a brief induction to Artificial Neural Networks (ANNs) for peo ple who have no prev ious knowledge o f them. W e first make a brie f. Applications of Artificial Neural Networks ANN Theory and Model ANNs are model of human brain developed artificially and they mimic the way brain processes information. The brain is a highly complex, non-linear, and parallel computer (information processing system) [7]. Basic building block of a brain is a nerve cell or a Size: KB. NASA Technical Memorandum Application of Artificial Neural Networks to Composite Ply Micromechanics D.A. Brown The College of Wooster Wooster, Ohio P.L.N. Murthy and L. Berke Lewis Research Center Cleveland, Ohio Prepared for the Engineering Mechanics Conference sponsored by the American Society of Civil Engineers Columbus, Ohio, May , (NAS A- TM- 36b) APPLICATION .

Application of artificial neural networks in micromechanics for polycrystalline metals. Artificial neural network crystal plasticity finite element framework is proposed. Y.C. Lin, J. Zhang, J. ZhongApplication of neural networks to predict the elevated temperature flow behavior of Cited by: 5. Get this from a library! Application of artificial neural networks to composite ply micromechanics. [D A Brown; P L N Murthy; Laszlo Berke; Lewis Research Center.]. Artificial Neural Networks (ANN), or simply Neural Networks (NN) are compu-tational systems inspired by the biological brain in their structure, data processing and restoring method, and learning ability. More specifically, a neural network is defined as a massively parallel distributed processor that has a natural propensity for storing ex-File Size: 2MB.   Neural Network Toolbox in MATLABNeural Network Toolbox™ provides tools fordesigning, implementing, visualizing, and simulating neuralnetworks. Neural networks are used for applications whereformal analysis would be difficult or impossible, such aspattern recognition and nonlinear system identification andcontrol.