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Exascale Computing: Accelerating the Path to Born-Qualified

ExaAM, a high-fidelity simulation tool, promises to reduce trial-and-error in additive manufacturing and help create metal 3D-printed parts that are qualified from the beginning.
Topics: Clean Energy Advanced Manufacturing

The science of metal additive manufacturing (AM) today is focused on the quest for born-qualified parts: components that can roll off the print bed and be ready for direct use, including in critical structures like vehicles, airplanes and power plants. To accelerate the work, the Department of Energy’s (DOE) national laboratories are getting ready to apply one of the country’s biggest science tools to the task: exascale computing.

Researchers around the country have made tremendous progress in AM during recent years, particularly in the printing of large and geometrically complex objects from new materials, including metals, polymers, ceramics and even biomaterials, fulfilling the promise of fully customizable parts, lower production cost and less time-to-market for American manufacturers.

But printing a large, complex part that is structurally sound in every way requires a massive number of calculations, taking into account all the physics of each specific material in every step of the process, from feedstock compounding to melting to extruding and re-solidification of each layer.

Today much of that work is being done through trial and error. You model a part, choose materials, approximate how the physics will work, print it, test it, figure out what worked and what didn’t, and start all over again, each time making adjustments such as electron beam current, laser power, or scanning patterns. “Additive manufacturing provides accurate control of many aspects of the process, but it can be extremely time-consuming to find the optimal settings, particularly for complex parts,” says John Turner, leader of the Computational Engineering & Energy Sciences Group at Oak Ridge National Laboratory (ORNL). “If, on the other hand, you perform some of that trial and error virtually, then you’ll need fewer experiments.”

“When you get to the point in AM when you want to quickly produce custom parts for a wide range of applications, you don’t have a lot of time for trial and error,” notes Suresh Babu, the University of Tennessee/ORNL Governor’s Chair of Advanced Manufacturing. “One way to solve it is to try monitor everything with sensors. But the problem is you can’t measure everything that’s buried in the printed object without actually pulling it apart.”

See the full article in Additive Manufacturing magazine … .