Additive manufacturing can produce parts with intricate shapes and minimal waste, offering excellent potential to redefine the development of metallic components.
But that potential is restricted by one crucial challenge: managing the defects during the procedure can affect the performance of 3D-printed materials.
A recent article published in the Additive Manufacturing journal has now revealed a potential breakthrough solution: temperature data can be used during production to estimate the formation of subsurface flaws, so that they can be tackled immediately.
A research team from the Argonne National Laboratory of the U.S. Department of Energy, along with a collaborator, who is currently at Texas A&M University, has identified 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. That’s the big-picture goal.
Aaron Greco, Study Author, Applied Materials Division, Argonne National Laboratory.
Greco is also the group manager for the Interfacial Mechanics & Materials team in the Applied Materials Division at Argonne National Laboratory.
In the latest study, the team employed the high-powered and highly bright X-rays at beamline 32-ID-B at Argonne National Laboratory’s Advanced Photon Source (APS), which is a Department of Energy Office of Science User Facility.
The researchers developed an experimental rig that enabled them to record temperature data from a typical infrared camera observing the printing procedure from above, while they concurrently applied a beam of X-rays taking a side-view to determine whether porosity was developing under the surface.
Porosity denotes minute and usually microscopic “voids” that can take place at the time of the laser printing procedure. Such tiny voids can make a component prone to a number of failures, including cracking.
Noah Paulson, a computational materials scientist in the Applied Materials Division and the study’s lead author, stated that this study has demonstrated that a correlation certainly exists between surface temperature and the formation of porosity 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.
Noah Paulson, Study Lead Author and Computational Materials Scientist, Applied Materials Division, Argonne National Laboratory
For instance, the researchers noted that thermal histories in which the peak temperature is low and then steadily decreases could be associated with low porosity. By contrast, thermal histories that begin high, dip, and subsequently increase can probably denote large porosity.
The team employed machine learning algorithms to decode the complex information and estimate the porosity formation from the thermal history.
According to Paulson, compared to the tools created by technological giants that employ scores of data points, this attempt had to manage with a couple hundred. “This required that we develop a custom approach that made the best use of limited data,” Paulson added.
Although 3D printers are usually integrated with infrared cameras, the complexity and cost render it unviable to equip a commercially available machine with the type of X-ray technology existing at the APS—one of the most robust X-ray light sources around the world. However, if a methodology is developed to probe systems that are already available in 3D printers, that would not be required.
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.
Ben Gould, Study Co-Author and Materials Scientist, Applied Materials Division, Argonne National Laboratory
The potential to detect and rectify flaws during the printing process would have significant implications for the whole additive manufacturing sector, because it would avoid the requirement for time-intensive and costly inspection procedures of each component produced at a large scale.
In conventional manufacturing, the process consistency renders it needless to scan each metallic part that comes 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,” added Greco. “To realize its full potential, we need to lower the risk to lower the cost.”
This attempt is made all the more urgent in detecting one of the major benefits that additive manufacturing has over conventional 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,” Greco added.
Gould added that going forward, the team is hoping that what he called a “very, very good first step” would enable it to keep expanding and enhancing the prototype.
“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,” Gould concluded.
The study was funded by Argonne’s Laboratory Directed Research and Development program.
Paulson, N. H., et al. (2020) Correlations between thermal history and keyhole porosity in laser powder bed fusion. Additive Manufacturing. doi.org/10.1016/j.addma.2020.101213.