Algorithm could shorten quality testing, research in many industries by
months
Machine learning used to predict direction-dependent mechanical
properties of metals

Date:
February 15, 2022
Source:
DOE/Sandia National Laboratories
Summary:
A machine-learning algorithm could provide auto manufacturing,
aerospace and other industries a faster and more cost-efficient
way to test bulk materials.



FULL STORY ==========================================================================
A machine-learning algorithm developed at Sandia National Laboratories
could provide auto manufacturing, aerospace and other industries a faster
and more cost-efficient way to test bulk materials.


==========================================================================
The technique was published recently in the scientific journal Materials Science and Engineering: A.

Production stoppages are costly. So, manufacturers screen materials like
sheet metal for formability before using them to make sure the material
will not crack when it is stamped, stretched and strained as it's
formed into different parts. Companies often use commercial simulation
software calibrated to the results of various mechanical tests, said
Sandia scientist David Montes de Oca Zapiain, the lead author on the
paper. However, these tests can take months to complete.

And while certain high-fidelity computer simulations can assess
formability in only a few weeks, companies need access to a supercomputer
and specialized expertise to run them, Montes de Oca Zapiain said.

Sandia has shown machine learning can dramatically cut time and resources
to calibrate commercial software because the algorithm does not need information from mechanical tests, said Montes de Oca Zapiain. Nor does
the method need a supercomputer. Additionally, it opens a new path to
perform faster research and development.

"You could efficiently use this algorithm to potentially find lighter
materials with minimal resources without sacrificing safety or accuracy," Montes de Oca Zapiain said.



========================================================================== Algorithm replaces mechanical tests The machine-learning algorithm
named MAD3, pronounced "mad cubed" and short for Material Data Driven
Design, works because metal alloys are made of microscopic, so-called "crystallographic" grains. Collectively, these grains form a texture that
makes the metal stronger in some directions than others, a phenomenon
that researchers call mechanical anisotropy.

"We've trained the model to understand the relationship between crystallographic texture and anisotropic mechanical response," Montes de
Oca Zapiain said. "You need an electron microscope to get the texture
of a metal, but then you can drop that information into the algorithm,
and it predicts the data you need for the simulation software without performing any mechanical tests." Teaming with Ohio State University,
Sandia trained the algorithm on the results of 54,000 simulated materials
tests using a technique called a feed-forward neural network. The Sandia
team then presented the algorithm with 20,000 new microstructures to test
its accuracy, comparing the algorithm's calculations with data gathered
from experiments and supercomputer-based simulations.

"The developed algorithm is about 1,000 times faster compared to
high-fidelity simulations. We are actively working on improving the
model by incorporating advanced features to capture the evolution of the anisotropy since that is necessary to accurately predict the fracture
limits of the material," said Sandia scientist Hojun Lim, who also
contributed to the research.

As a national security laboratory, Sandia is conducting further research
to explore whether the algorithm can shorten quality assurance processes
for the U.S. nuclear stockpile, where materials must meet rigorous
standards before being accepted for production use. The National Nuclear Security Administration funded the machine-learning research through
the Advanced Simulation and Computing program.

To enable other institutions to take advantage of the technology,
Sandia formed a cross-disciplinary team to develop the user-friendly, graphics-based Material Data Driven Design software. It was developed
with input from more than 75 interviews with potential users through
the Department of Energy's Energy I- Corps program.

========================================================================== Story Source: Materials provided by
DOE/Sandia_National_Laboratories. Note: Content may be edited for style
and length.


========================================================================== Journal Reference:
1. David Montes de Oca Zapiain, Hojun Lim, Taejoon Park, Farhang
Pourboghrat. Predicting plastic anisotropy using crystal plasticity
and Bayesian neural network surrogate models. Materials Science and
Engineering: A, 2022; 833: 142472 DOI: 10.1016/j.msea.2021.142472 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220215125521.htm

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