Machine learning assisted calibration of a ductile fracture locus model

Sandra Baltic, Mohammad Zhian Asadzadeh, Patrick Hammer, Julien Magnien, Hans Peter Gänser, Thomas Antretter, René Hammer

Publikation: Beitrag in FachzeitschriftArtikelForschungBegutachtung

2 Zitate (Scopus)


While several different specimen geometries are typically required to calibrate a ductile fracture locus model, this article presents for the first time a calibration methodology that uses one single specimen geometry. This is accomplished by a computational framework that combines finite element modelling (FEM) and artificial neural network (ANN). The combinations of the model parameters are used to generate the training database. The local displacement fields and global force-displacement histories are extracted throughout the complete numerical experiment and passed to the ANN. Therefore, the influence of the local stress state on the evolution of the local deformation is implicitly taken into account. The trained ANN is verified by evaluating its predictability of material parameters of FE simulations unseen in the training stage. The experimental data obtained from the shear tensile test using Digital Image Correlation is introduced to the trained ANN to identify the parameter set that predicts the real mechanical response of the shear specimen. Three different ANN architectures with distinguished input representations are studied. It turns out that all of them can acceptably describe the experimental behaviour of not only the calibration specimen but also the specimens not used for training the model.
FachzeitschriftMaterials and Design
Frühes Online-Datum21 Feb. 2021
PublikationsstatusVeröffentlicht - Mai 2021

Bibliographische Notiz

Funding Information:
The authors gratefully acknowledge the financial support under the scope of the COMET program within the K2 Center “Integrated Computational Material, Process and Product Engineering (IC-MPPE)” (Project No 859480 ). This program is supported by the Austrian Federal Ministries for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK) and for Digital and Economic Affairs (BMDW) , represented by the Austrian research funding association (FFG), and the federal states of Styria, Upper Austria and Tyrol. TDK Electronics is thanked for providing the material. The help of Dr. Johann Riedler with programming is gratefully acknowledged.

Publisher Copyright:
© 2021 The Author(s)

Dieses zitieren