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Abstract

The application of numerical simulations to optimise manufacturing processes, such as sheet metal forming, is currently state of the art. However, the behaviour of materials may change during such processes due to the evolution of damage. Therefore, for the proper estimation of the material behaviour, damage evolution has to be coupled with constitutive relationships. At the current state, there are several damage models which are able to describe such damage evolution on a macro level.

However, for a more accurate estimation it is crucial to take microstructural information, such as texture and grain size distribution, into account. A homogenisation from micro- to macro-schemes is computationally expensive when conducted with finite element methods. Hence, a new approach using a machine learning based framework is suggested, which can map damage from the micro- to the macro level.

In the scope of this work, the numerical data based on finite element simulations is used to train the machine learning algorithm. This data is generated using a microstructurally informed synthetic representative volume element (RVE). The material model consists of phenomenological crystal plasticity as well as damage evolution based on equivalent plastic strain. Local quantities from these RVE simulations (e.g. stress, strain and damage) are homogenised into global averages. The global damage evolution is then predicted as a function of macroscopic parameters (e.g. equivalent strains, equivalent and hydrostatic stresses) and material properties (e.g Young's modulus and Poisson's ratio) by the trained machine learning algorithm. Two different machine learning algorithms, support vector machines and random forest, are used and compared with each other.