AI software enables real-time 3D printing quality assessment

Date:
August 14, 2020
Source:
DOE/Oak Ridge National Laboratory
Summary:
Researchers have developed artificial intelligence software for
powder bed 3D printers that assesses the quality of parts in real
time, without the need for expensive characterization equipment.



FULL STORY ==========================================================================
Oak Ridge National Laboratory researchers have developed artificial intelligence software for powder bed 3D printers that assesses the quality
of parts in real time, without the need for expensive characterization equipment.


==========================================================================
The software, named Peregrine, supports the advanced manufacturing
"digital thread" being developed at ORNL that collects and analyzes
data through every step of the manufacturing process, from design to
feedstock selection to the print build to material testing.

"Capturing that information creates a digital 'clone' for each part,
providing a trove of data from the raw material to the operational
component," said Vincent Paquit, who leads advanced manufacturing data analytics research as part of ORNL's Imaging, Signals and Machine Learning group. "We then use that data to qualify the part and to inform future
builds across multiple part geometries and with multiple materials,
achieving new levels of automation and manufacturing quality assurance."
The digital thread supports the factory of the future in which custom
parts are conceived using computer-aided design, or CAD, and then produced
by self- correcting 3D printers via an advanced communications network,
with less cost, time, energy and materials compared with conventional production. The concept requires a process control method to ensure
that every part rolling off printers is ready to install in essential applications like cars, airplanes, and energy facilities.

To devise a control method for surface-visible defects that would work on multiple printer models, ORNL researchers created a novel convolutional
neural network -- a computer vision technique that mimics the human
brain in quickly analyzing images captured from cameras installed on the printers. The Peregrine software uses a custom algorithm that processes
pixel values of images, taking into account the composition of edges,
lines, corners and textures. If Peregrine detects an anomaly that may
affect the quality of the part, it automatically alerts operators so adjustments can be made.

The software is well suited to powder bed printers. These printers
distribute a fine layer of powder over a build plate, with the material
then melted and fused using a laser or electron beam. Binder jetting
systems rely on a liquid binding agent rather than heat to fuse powdered materials.



==========================================================================
The systems print layer by layer, guided by the CAD blueprint, and are
popular for the production of metal parts. However, during the printing process, problems such as uneven distribution of the powder or binding
agent, spatters, insufficient heat, and some porosities can result in
defects at the surface of each layer. Some of those issues may happen in
such a very short timeframe that they may go undetected by conventional techniques.

"One of the fundamental challenges for additive manufacturing is that
you're caring about things that occur on length-scales of tens of microns
and happening in microseconds, and caring about that for days or even
weeks of build time," said ORNL's Luke Scime, principal investigator
for Peregrine.

"Because a flaw can form at any one of those points at any one of those
times, it becomes a challenge to understand the process and to qualify a
part." Peregrine is being tested on multiple printers at ORNL, including
as part of the Transformational Challenge Reactor (TCR) Demonstration
Program that is pursuing the world's first additively manufactured nuclear reactor. TCR is leveraging ORNL's rich history in nuclear science and engineering, materials science and advanced manufacturing to develop a microreactor with newer materials in less time at a lower cost, ensuring
the future of this important carbon-free energy source.

"For TCR in particular, you could have a scenario in which the regulator
will want detailed data on how a part was manufactured, and we can
provide specs with the database built using Peregrine," Scime said.

"Establishing correlations between these signatures collected
during manufacturing and performance during operation will be the
most data-rich and informed process for qualifying critical nuclear
reactor components," said Kurt Terrani, TCR program director. "The
fact that it may be accomplished during manufacturing to eliminate the
long and costly conventional qualification process is the other obvious benefit." ORNL researchers stress that by making the Peregrine software machine-agnostic -- able to be installed on any powder bed system --
printer manufacturers can save development time while offering an improved product to industry. Peregrine produces a common image database that can
be transferred to each new machine to train new neural networks quickly,
and it runs on a single high-powered laptop or desktop. Standard cameras
were used in the research, ranging in most cases from 4 to 20 megapixels
and installed so they produce images of the print bed at each layer. The software has been tested successfully on seven powder bed printers at
ORNL so far, including electron beam melting, laser powder bed, and
binder jetting, as detailed in the journal Additive Manufacturing.



========================================================================== "Anything we can do to help operators and designers know what works
and what doesn't helps with the confidence that the part will be okay
for use," Scime said. "When you have a 3D map of every pixel where
the network thinks there is an anomaly and what it thinks the problem
is, it opens up a whole world of understanding of the build process."
As the monitoring system has evolved, Scime said researchers are able to combine the image data with data from other sources such as the printer's
log files, the laser systems and operator notes, allowing parts to be
uniquely identified and statistics from all parts tracked and evaluated.

The AI software was developed at the Manufacturing Demonstration Facility
at ORNL, a U.S. Department of Energy user facility that works closely
with industry to develop, test and refine nearly every type of modern
advanced manufacturing technology.

"There's no place else like the MDF where this machine-agnostic algorithm
could have been developed, simply because we have so many machines and so
many builds going on all the time in the course of our research," Scime
said. "Access to data is key. Here, we have the ability to place sensors
easily and the technicians to make sure everything works and that we're
getting our data. With the variety of scientific expertise available here,
it's been easy to find experts to help with all the challenges involved."
In other process control work, MDF researchers are developing methods to monitor for defects on the subsurface of builds and to detect porosity
that may form in deeper layers, including the use of photodiodes and
high-speed cameras.

"We've been doing welding for hundreds of years, but additive has only
been around for a couple of decades and we don't know what the problems
look like in some cases," Scime said. "Machine learning techniques allow
us to collect and analyze a lot of data quickly. We can then identify
those problems and gain the knowledge we need to better understand and
prevent anomalies."

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


========================================================================== Journal Reference:
1. Luke Scime, Derek Siddel, Seth Baird, Vincent Paquit. Layer-wise
anomaly
detection and classification for powder bed additive manufacturing
processes: A machine-agnostic algorithm for real-time pixel-wise
semantic segmentation. Additive Manufacturing, 2020; 36: 101453 DOI:
10.1016/ j.addma.2020.101453 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200814163305.htm

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