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TECHNICAL PAPERS

Determination of Poisson’s Ratio by Spherical Indentation Using Neural Networks—Part I: Theory

[+] Author and Article Information
N. Huber

Forschungszentrum Karlsruhe, Institut für Materialforschung II (IMF II), Postfach 3640, D-76021 Karlsruhe, Germanye-mail: norbert.huber@imf.fzk.de

A. Konstantinidis

Laboratory of Mechanics and Materials, Aristotle University of Thessaloniki, GR-54006 Thessaloniki, Hellas, Greecee-mail: avraam@mom.gen.auth.gr

Ch. Tsakmakis

Technische Universität Darmstadt, Institut für Mechanik 1, Hochschulstrasse 1, D-64289 Darmstadt, Germanye-mail: tsakmakis@mechanik.tu-darmstadt.de

J. Appl. Mech 68(2), 218-223 (Nov 01, 2000) (6 pages) doi:10.1115/1.1354624 History: Received March 26, 1999; Revised November 01, 2000
Copyright © 2001 by ASME
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References

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Figures

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Sketch of the geometry of spherical indentation with small overall plastic deformation
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Sketch of a spherical indentation depth-load response for elastic-plastic deformation
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Finite element mesh for spherical indentation with low loads
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Depth-load trajectories for the the finite element meshes M1–M3: E=200 GPa, ν=013,k0=250 MPa, ET=10 GPa (a<2.2 mm)
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Examples of finite element simulations for Er*=200 GPa, k0=500 MPa, ET=20 GPa, ht=4hy*
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The effect of ν and ET/Er* on Π2 for Π1*=const
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The effect of ν and ET/Er* on Π3 for Π1*=const
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Sketch of a multilayer feed forward neural net
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Correlation of Π2 and Π3
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Identification of ν on the basis of one unloading
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Identification of ν on the basis of two unloadings
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Dependency of identified Poisson’s ratio ν̃(h̃y*) using Set 1 and Set 2

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