Promising new research identifies novel approach for controlling defects
in 3D printing

Date:
August 5, 2020
Source:
DOE/Argonne National Laboratory
Summary:
Scientists use temperature data to tune -- and fix -- defects in
3D- printed metallic parts.



FULL STORY ==========================================================================
With its ability to yield parts with complex shapes and minimal waste,
additive manufacturing has the potential to revolutionize the production
of metallic components. That potential, however, is currently limited
by one critical challenge: controlling defects in the process that can compromise the performance of 3D-printed materials.


==========================================================================
A new paper in the journal Additive Manufacturing points to a possible breakthrough solution: Use temperature data at the time of production
to predict the formation of subsurface defects so they can be addressed
right then and there. A team of researchers at the U.S. Department of
Energy's (DOE) Argonne National Laboratory, together with a colleague
now at Texas A&M University, discovered the possibility.

"Ultimately you would be able to print something and collect temperature
data at the source and you could see if there were some abnormalities,
and then fix them or start over," said Aaron Greco, group manager
for Argonne's Interfacial Mechanics & Materials group in the Applied
Materials Division (AMD) and a study author. "That's the big-picture
goal." For their research, the scientists used the extremely bright, high-powered X- rays at beamline 32-ID-B at Argonne's Advanced Photon
Source (APS), a Department of Energy Office of Science User Facility. They designed an experimental rig that allowed them to capture temperature
data from a standard infrared camera viewing the printing process from
above while they simultaneously used an X-ray beam taking a side-view
to identify if porosity was forming below the surface.

Porosity refers to tiny, often microscopic "voids" that can occur during
the laser printing process and that make a component prone to cracking
and other failures.

According to Noah Paulson, a computational materials scientist in the
Applied Materials division and lead author on the paper, this work
showed that there is in fact a correlation between surface temperature
and porosity formation below.



========================================================================== "Having the top and side views at the same time is really powerful. With
the side view, which is what is truly unique here with the APS setup,
we could see that under certain processing conditions based on different
time and temperature combinations porosity forms as the laser passes
over," Paulson said.

For example, the paper observed that thermal histories where the peak temperature is low and followed by a steady decline are likely to
be correlated with low porosity. In contrast, thermal histories that
start high, dip, and then later increase are more likely to indicate
large porosity.

The scientists used machine learning algorithms to make sense out of
the complex data and predict the formation of porosity from the thermal history.

Paulson said that in comparison to the tools developed by tech giants
that use millions of data points, this effort had to make do with a
couple hundred.

"This required that we develop a custom approach that made the best use
of limited data," he said.

While 3D printers typically come equipped with infrared cameras, the
cost and complexity make it impossible to equip a commercial machine
with the kind of X- ray technology that exists at the APS, which is one
of the most powerful X-ray light sources in the world. But by designing
a methodology to observe systems that already exist in 3D printers,
that wouldn't be necessary.

"By correlating the results from the APS with the less detailed results we
can already get in actual printers using infrared technology, we can make claims about the quality of the printing without having to actually see
below the surface," explained co-author Ben Gould, a materials scientist
in the AMD.

The ability to identify and correct defects at the time of printing
would have important ramifications for the entire additive manufacturing industry because it would eliminate the need for costly and time-consuming inspections of each mass-produced component. In traditional manufacturing,
the consistency of the process makes it unnecessary to scan every metallic component coming off of the production line.

"Right now, there's a risk associated with 3D printing errors, so that
means there's a cost. That cost is inhibiting the widespread adoption
of this technology," Greco said. "To realize its full potential, we
need to lower the risk to lower the cost." This effort is made all
the more urgent in recognizing one of the key advantages that additive manufacturing has over traditional manufacturing. "We saw with the recent pandemic response how valuable it would be to be able to quickly adapt production to new designs and needs. 3D technology is very adaptable to
those kinds of changes," added Greco.

Looking ahead, Gould said the research team was hopeful that what he
called a "very, very good first step" would allow it to keep improving
and expanding the model. "For machine learning, to build accurate models
you need thousands and thousands of data points. For this experiment,
we had 200. As we put in more data, the model will get more and more
exact. But what we did find is very promising."

========================================================================== Story Source: Materials provided by
DOE/Argonne_National_Laboratory. Original written by Brett Hansard. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Noah H. Paulson, Benjamin Gould, Sarah J. Wolff, Marius Stan,
Aaron C.

Greco. Correlations between thermal history and keyhole porosity
in laser powder bed fusion. Additive Manufacturing, 2020; 34:
101213 DOI: 10.1016/ j.addma.2020.101213 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200805181730.htm

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