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Now that metal additive manufacturing (AM) is creating fully functional industrial parts, many OEMs are taking a closer look at how the technology might support their individual production goals. Interest has also been piqued by the commitment to AM of some major companies. “I think the news about the GE Leap engine fuel nozzle really resonated throughout industry,” said Doug Hedges, President and COO of Sintavia LLC, a metal AM service provider for aerospace, defense, and other industries. “That got everyone's attention, and certainly increased the pace of inquiries for us.” The nozzle, produced internally at GE, was the first 3D-printed part certified by the U.S. Federal Aviation Administration (FAA) to fly inside a commercial jet engine.
“Additive manufacturing is a very challenging field,” said Hedges. “We felt we needed to enter it in the early stages — rather than wait until the industry was more mature — in order to refine our skills.” Their AM resources now include five machines from three of the leading metal manufacturers, as well as an electron beam melting (EBM) system.
While metal AM is at the center of Sintavia's offerings, their core competencies also include full material characterization; finishing processes such as heat treatment, HIPing (Hot Isostatic Pressing), and CNC machining; and CT scanning to inspect the integrity of the final product. After five-plus years in operation, Sintavia's client roster is currently about 75-80% aerospace, as well as oil and natural gas, automotive, and turbomachinery for power generation. Clients are interested in AM research and development, and production of finished parts.
Their aerospace customers focus on everything from aircraft to satellites to weapons, including flight hardware for a Boeing 787, or hardware that goes on an unmanned vehicle. “To qualify parts for flight hardware of any kind is a very intensive thing, but for human travel, the substantiation is a lot higher. The better monitoring of manufacturing you have, the better inspection you have, and the more assurance you're going to have to say this product is capable of flight usage,” Hedges said.
Many of the same materials used in aerospace are also employed by Sintavia customers from other industries; for example, an oil and gas equipment supplier may want a wellhead cap, a sensor, or a tool that needs to be extremely corrosion-resistant. They use the same superalloys as those used in jet engines, such as Inconel 625 or 718. Additive manufacturing can make such superalloys easier to work with; AM allows creation of complex interior channels directly within a build, cutting down considerably on machining time.
Mastering the Complexity of AM
Sintavia's latest Direct Metal Laser Melting (DMLM) machine acquisition was a Concept Laser M2 cusing system. German AM provider Concept Laser's QM Meltpool 3D technology monitors specific process parameters of a LaserCUSING® build as it's actually in progress. A series of semi-permeable mirrors is positioned to both steer the path of the laser (following the CAD geometry of the object being printed), and reflect back emissions data (thermal radiation) about the meltpool in progress to an on-axis camera and a photodiode. This setup keeps the sensors out of the actual build chamber — an important safety benefit. The combination of camera and photodiode provides much greater monitoring precision than other AM systems that use a single photodiode alone. The thermal detection data is correlated with the toolpath of the laser spot as it melts the metal. This allows for the identification of any deviations from the set requirements for laser energy, meltpool heat intensity, powder material thickness, and so on.
The data continually runs through software that calculates the average emitted meltpool intensity and area per build layer, providing a moment-by-moment, high-resolution visual prediction of the process. Volume Graphics’ data analysis and visual analysis software then combines the input from multiple laser passes into a voxel image of the part and its internal structure. This gives the operator a continuous, three-dimensional view of the growing part, and enables them to detect any potential for irregularities in the build as it progresses.
Shortly after acquiring the Concept Laser machine, Sintavia purchased a 5-axis Nikon CT scanner, and an opportunity arose to compare results from meltpool monitoring with CT scanning when Concept Laser approached Sintavia to offer support for a joint study on the relationship between the two technologies. Sintavia, in turn, wanted to strengthen their quality-assurance capabilities by exploring the value of meltpool monitoring and CT in tandem. The Concept Laser M2 cusing and the Nikon CT scanner share the same software — an advantage that helps avoid any interoperability issues.
CT scanning — a valuable tool for assessing traditionally manufactured products in certain industries — is already a requirement for most critical parts. It allows both internal and external geometries to be seen, detects voids, confirms densities, and performs extremely high-value metrology for first-article dimensional inspection. CT scanning, however, is performed after a product is manufactured; any defects detected at that stage must be assessed as to whether they affect a part's integrity and, if so, corrected if possible, or discarded. This costs both time and resources; the latter can be considerable when working with superalloys.
Meltpool monitoring, on the other hand, can identify potential problems at any point during an AM build, providing the manufacturer with opportunities to halt the run and change settings, or even scrap the entire setup before additional material is wasted. In terms of initial outlay, meltpool monitoring is less expensive than CT by a factor of about ten, and it has no continuous operation costs once set up.
Starting this project, the fundamental questions Sintavia wanted to answer were if data from in-situ monitoring was similar to a CT scan and, if so, if it could be used to qualify parts. Long term, Sintavia's goal for serial production with AM was to be able to build a reference job with active QM Meltpool 3D, and then investigate finished part quality by using CT or crosssections. The QM Meltpool 3D data of the reference parts can then be compared with all further build jobs of the same design. If there are no significant incidents during the build job, there is no need for additional and expensive post-process examinations.
As Sintavia began setting up their trials, they realized that given the high accuracy of Concept Laser's stock parameters with the M2 cusing machine, they would have to create artificial build errors in order to assess QM Meltpool 3D's detection capabilities. First, they designed three different shapes to work with: an organic sphere based on the structure of a rocket fuel particle, a canted helix, and a series of density cylinders with artificial pores ranging in diameter from 30 µm to 2 mm. Then they deliberately created irregularities in the powder bed process due to factors such as uncalibrated scaling, overexposure, a cold pause, a too-thick recoat layer, and other changes in parameters that might create build flaws; in this case, focusing solely on voids. The finished shapes were then CT scanned to determine if the voids predicted by QM Meltpool3D actually occurred.
Not every deviation that meltpool monitoring detects in a single AM layer will necessarily lead to a void that would cause a part to be scrapped. Subsequent powder layers melted by laser input sometimes “heal” small deviations as the process continues. In this study Sintavia considered predictions for voids that were five times the size of QM meltpool resolution (5x70µm = 350µm). The QM Meltpool 3D can detect irregularities that start as small as 70 microns, which is the resolution of the data.
The final data determined that the QM Meltpool 3D predicted the flaws that CT detected. This was Sintavia's first trial look at quality meltpool monitoring, and the results are important. It's a continuing process that will take more work by Sintavia to completely understand the relationship between meltpool monitoring and CT scanning.
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