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Identification of viscoplastic material parameters from spherical indentation data: Part II. Experimental validation of the method

Published online by Cambridge University Press:  01 March 2006

D. Klötzer,
Ch. Ullner,
E. Tyulyukovskiy and
N. Huber
D. Klötzer
Affiliation:
Bundesanstalt für Materialforschung und -prüfung, 12200 Berlin, Germany
Ch. Ullner*
Affiliation:
Bundesanstalt für Materialforschung und -prüfung, 12200 Berlin, Germany
E. Tyulyukovskiy
Affiliation:
Forschungszentrum Karlsruhe, Institut für Materialforschung II, 76021 Karlsruhe, Germany
N. Huber
Affiliation:
Forschungszentrum Karlsruhe, Institut für Materialforschung II, 76021 Karlsruhe, Germany
*
a) Address all correspondence to this author. e-mail: christian.ullner@bam.de
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Abstract

A neural network-based analysis method for the identification of a viscoplasticity model from spherical indentation data, developed in the first part of this work [J. Mater. Res.21, 664 (2006)], was applied for different metallic materials. Besides the comparison of typical parameters like Young’s modulus and yield stress with values from tensile experiments, the uncertainties in the identified material parameters representing modulus, hardening behavior, and viscosity were investigated in relation to different sources. Variations in the indentation position, tip radius, force application rate, and surface preparation were considered. The extensive experimental validation showed that the applied neural networks are very robust and show small variation coefficients, especially regarding the important parameters of Young’s modulus and yield stress. On the other hand, important requirements were quantified, which included a very good spherical indenter geometry and good surface preparation to obtain reliable results.

Keywords

Elastic propertiesHardnessStress/strain relationship

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Type
Articles
Information
Journal of Materials Research , Volume 21 , Issue 3 , March 2006, pp. 677 - 684
Copyright
Copyright © Materials Research Society 2006

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References

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