As the use of AM increases as a large-scale manufacturing technique, opportunities abound for parallel growth in the use of CT.
At the same time, identifiable challenges remain. Some are physics based — for instance, how X-rays interact with different materials — while others are technologically based, such as when current diagnostic systems are too small or not fast enough. And finally, there is also a human element. CT systems are complex, scientific instruments. CT imaging typically requires well educated, highly trained specialists to correctly operate, maintain, modify and troubleshoot the machines. One of the biggest challenges with automated CT today is the need to simplify the operation so that less specialized users can operate machines just as effectively.
Challenges Presented by Continuing Manufacturing Innovation
Some challenges are moving targets. For example, thanks to ongoing research, it’s becoming possible to 3D print larger and larger parts in shorter times, meaning that CT machines need to both accommodate larger parts and have faster throughput. Some researchers and practitioners are challenging the current ASTM definition of AM by printing 3D shapes all at once, rather than by a gradual layering process, which would also mean that CT evaluation of each part needs to occur more quickly.
Experiments and Requirements for High Energy
Due to experimentation and success of using new materials for AM, some parts are now being made of heavier and denser elements than before. This introduces more physics-based challenges because such materials require high acceleration voltages for X-rays to penetrate them, and high current settings to get an acceptable transmitted signal on the other side of the part. As a result, a relatively large focal spot size is generated, which results in less clear images.
Other issues involve current testing paradigms. Typically, AM test artifacts are not designed with CT in mind. For example, a NIST (U.S. National Institute of Standards and Technology) artifact originally designed to test AM printer capabilities has too high an aspect ratio, causing difficulties in CT imaging. Conversely, many existing CT “phantoms” (objects used as stand-ins to test and calibrate CT machines) were not created using AM, so are not suitable for tuning CT machines for their designated AM uses.
Speed of Production for 3D CT Imaging
Imaging at the speed of production remains one of the most significant blockages in 3D CT scanning today.
This is mainly because CT analysis itself comprises sets of complex software algorithms that process large amounts of data collected by X-ray detectors.
Inline production and inspection means that critical components with complex material structures or internal geometries are inspected at the same rate they are being produced. No time-lag occurs, maximizing production and product sign-off efficiency. So, if a production line is making a part every 30 seconds, maximum efficiency would require an automated inspection to take place within that same 30 seconds. Technologies that enhance both speed and function at that the development of automated inspection are, in some ways, still in their infancy.
How 3D CT Imaging Technology has Improved
Despite the challenges presented enough, use of 3D CT in these contexts is becoming more and more possible. Just five years ago, it could have taken hours, even days for detectors to run their software algorithms, create 3D images and analyze the data. Now, days have become hours and hours have become minutes, so it’s not unreasonable to expect those minutes to be further reduced to seconds.
The technological hurdles we’ve described can be overcome, as they have in the past, by cooperation among CT manufacturers, systems integrators and their customers.
Next Steps for 3D CT
Researchers and industry advocates who understand the intersection of these two complementary technologies — AM on the one hand, and CT, on the other — are beginning to work together to solve these issues and push the technology further.
For example, the Advanced Casting Research Center (ACRC) at the University of California, Irvine, a consortium of over 30 manufacturers and research entities, is engaged in projects as diverse as innovative AM techniques and the application of big data to manufacturing process improvement. Collaboration is also growing among traditionally separated AM and CT research and standards groups such as in subcommittees of the ASTM. Work of this sort will do much to iron out existing and future issues.
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