Predicting the elasto-plastic response of short fiber composites using deep neutral networks trained on micro-mechanical simulations
Typ
Examensarbete för masterexamen
Program
Applied mechanics (MPAME), MSc
Publicerad
2021
Författare
Friemann, Johan
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The mechanical modeling of short fiber composites has proven to be difficult. This is partly owing to
the high degree of anisotropy and fiber discontinuity. Being able to accurately predict the behavior
of short fiber composites with varying fiber orientations and fiber volume fractions is highly relevant
in the design and production of injection molded parts. It has therefore become popular to abandon
classical constitutive models in favor of data driven models. Artificial neural networks is a popular
and efficient method of using large amounts of data to teach an algorithm the underlying rules of
a phenomenon, enabling it to make predictions for data it has never before encountered. In this
work a recurrent deep neural network model utilizing Gated Recurrent Units (GRU) is trained to
predict the elasto-plastic stress response of a short fiber composite material given the strain path.
The model is designed to have the ability to make predictions for arbitrary fiber orientations and
varying fiber volume fractions. The training data is generated by performing micro-mechanical
simulations utilizing mean field methods in the commercially available software digimat-mf. The
strain data is generated by a random walk scheme in strain space. The finished model performs
well, and the mean error for a typical load cycle usually stays below 10% of the matrix yield stress.
Beskrivning
Ämne/nyckelord
Composite mechanics , Short fiber composites , Material mechanics , Micro mechanics , Elasto-plasticity , Mean field homogenization , Artificial neural networks , Deep neural networks , Recurrent neural networks