DOE Awards DMG Mori $400K to Develop AI Tool for Faster 3D Printing Part Qualification

DOE Backs AI for Metal 3D Printing

The U.S. Department of Energy (DOE) has awarded DMG Mori Federal Services $400,000 to develop an AI-powered optimization tool for powder bed fusion (PBF) 3D printing processes. The project will collaborate with Oak Ridge National Laboratory (ORNL), which hosts some of the world's most powerful supercomputing systems.

The award is part of the HPC4Mfg (High-Performance Computing for Manufacturing) program, funded through the DOE's Office of Critical Minerals and Energy Innovation. Since 2015, the program has funded projects that leverage national laboratory HPC capabilities to optimize manufacturing processes.

Accelerating Part Qualification

The goal of the project is to automate the optimization of PBF processes, potentially reducing the time it takes to qualify 3D printed parts for critical applications. Traditional part qualification in metal additive manufacturing can be extremely time-consuming, requiring extensive testing and validation.

"The broad-sweeping goal of the R&D program is to accelerate the qualification of engineered systems that use AM parts," according to the project description.

ORNL Partnership

Oak Ridge National Laboratory has long been at the forefront of additive manufacturing R&D, hosting some of the world's most powerful supercomputers. The collaboration with DMG Mori represents another step toward bringing AI and machine learning into industrial 3D printing workflows.

This isn't the first time ORNL has worked on AI for additive manufacturing. The laboratory previously developed an AI tool that takes the guesswork out of 3D printing, and has collaborated with ZEISS on AI-enabled X-ray computed tomography for accelerated part qualification.

The DMG Mori project could have significant implications for industries that rely on metal 3D printing, including aerospace, defense, and energy sectors where part qualification timelines are critical.