In a previous paper it has been shown that the load and the unloading stiffness are, among others, explicit functions of the Poisson’s ratio, if a spherical indenter is pressed into a metal material. These functions can be inverted by using neural networks in order to determine the Poisson’s ratio as a function of the load and the unloading stiffness measured at different depths. Also, the inverse function possesses as an argument the ratio of the penetration depth and that depth, at which plastic yield occurs for the first time. The latter quantity cannot be measured easily. In the present paper some neural networks are developed in order to identify the value of Poisson’s ratio. After preparing the input data appropriately, two neural networks are trained, the first one being related to Set 2 of the previous paper. In order to avoid an explicit measurement of the yield depth, the second neural network has to be trained in such a way, that its solution intersects with that of Set 2 at the correct value of Poisson’s ratio. This allows to identify Poisson’s ratio with high accuracy within the domain of finite element data.

1.
Huber
,
N.
, and
Tsakmakis
,
Ch.
,
2001
, “
Determination of Poisson’s Ratio by Spherical Indentation Using Neural Networks. Part I: Theory
,”
ASME J. Appl. Mech.
,
68
, pp.
218
223
.
2.
Huber
,
N.
, and
Tsakmakis
,
Ch.
,
1999
, “
Determination of Constitutive Properties From Spherical Indentation Data Using Neural Networks, Part I: The Case of Pure Kinematic Hardening in Plasticity Laws
,”
J. Mech. Phys. Solids
,
47
, pp.
1569
1588
.
3.
Huber
,
N.
, and
Tsakmakis
,
Ch.
,
1999
, “
Determination of Constitutive Properties From Spherical Indentation Data Using Neural Networks, Part II: Plasticity With Nonlinear Isotropic and Kinematic Hardening
,”
J. Mech. Phys. Solids
,
47
, pp.
1589
1607
.
You do not currently have access to this content.