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.
Determination of Poisson’s Ratio by Spherical Indentation Using Neural Networks—Part II: Identification Method
Contributed by the Applied Mechanics Division of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS for publication in the ASME JOURNAL OF APPLIED MECHANICS. Manuscript received by the ASME Applied Mechanics Division, Mar. 26, 1999; final revision, Nov. 1, 2000. Associate Editor: K. T. Ramesh. Discussion on the paper should be addressed to the Editor, Professor Lewis T. Wheeler, Department of Mechanical Engineering, University of Houston, Houston, TX 77204-4792, and will be accepted until four months after final publication of the paper itself in the ASME JOURNAL OF APPLIED MECHANICS.
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Huber, N., and Tsakmakis, C. (November 1, 2000). "Determination of Poisson’s Ratio by Spherical Indentation Using Neural Networks—Part II: Identification Method ." ASME. J. Appl. Mech. March 2001; 68(2): 224–229. https://doi.org/10.1115/1.1355032
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