Determination of deformation and failure properties of ductile materials by means of the small punch test and neural networks
This paper describes an approach to identify deformation and failure properties of ductile materials. The experimental method of the small punch test is used to determine the material response under loading. The resulting load displacement curve is transfered to a neural network, which was trained using load displacement curves generated by finite element simulations of the small punch test and the corresponding material parameters. The simulated material behavior of the specimen is based on the ductile elastoplastic damage theory of Gurson, Tvergaard and Needleman. During a training process the neural network generates an approximated function for the inverse problem relating the material parameters to the shape of the load displacement curve of the small punch test. This technique was tested for three different materials (ductile steels). The identified parameters are verified by testing and simulating notched tensile specimens.
small punch test, neural networks, ductile materials, finite elements, parameter identification
M. Kuna, M.Abendroth, Determination of deformation and failure properties of ductile materials by means of the small punch test and neural networks, Computational Material Science, Computational Material Science, 28, 633-644, 2003