Deep material networks for lead-free solder alloys
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Applied mechanics (MPAME), MSc
Publicerad
2023
Författare
Juhlin Onbeck, Gustav
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
In this report the Deep Material Network (DMN) method is applied to a microstructural
model for SAC-305, SnAgCu with 3 weight percent silver and 0.5 weight percent copper,
generated by a phase field simulation for spinodal decomposition. The DMN method homog enizes a heterogeneous microstructure for multiscale simulation purposes. Five microstruc tures from the phase-field simulations were generated, and for each micro structure a data set
describing the homogenized elastic parameters was calculated. The data sets were generated
using finite element simulations for each morphology. The DMN method is modified to use
individual weights for each node and a bias, the modifications improved the accuracy of the
network and brings the method more inline with conventional neural network structures. The
new DMN method was evaluated on data sets for each of the individual microstructures and
on combinations of multiple microstructure data sets. DMNs trained on data sets of multiple
microstructures resulted in a much greater error than DMNs trained on individual data sets.
The DMNs trained on a single microstructure reached a relative error of 2.5 - 3 %, whereas
a DMN trained on a multi-microstructure set could at best reach an error of 11.5 %.
Beskrivning
Ämne/nyckelord
Machine learning, deep material network, multi-scale simulations, lead-free solder alloys.