ORNL’s Additive Manufacturing Datasets Now Available

The U.S. Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL), Oak ridge, Tennessee, has released a new set of metal additive manufacturing (AM) data that industry and researchers can use to evaluate and improve the quality of AM components. The data can significantly boost efforts to verify the quality of the parts using only information gathered during AM, without requiring expensive and time-consuming post-production analysis.

AM part and its digital sample.

An AM part is sliced into small pieces, each of which is tested for tensile strength. A digital copy of the same AM part is analyzed by an AI model to locate anomalies within its structure.

Data has been routinely captured over a decade at DOE’s Manufacturing Demonstration Facility (MDF) at ORNL, where early-stage research in advanced manufacturing, coupled with comprehensive analysis of the resulting components, is reported to have created a vast trove of information about how AM machines perform.

The conventional manufacturing industry benefits from centuries of quality-control experience. However, AM is a newer, non-traditional approach that typically relies on expensive evaluation techniques for monitoring the quality of parts. These techniques might include destructive mechanical testing or non-destructive X-ray computed tomography, which creates detailed cross-sectional images of objects without damaging them. Although informative, these techniques have limitations, for example, they are difficult to perform on large parts. ORNL’s AM data can be used to train machine learning models to improve quality assessment for any type of component.

 “We are providing trustworthy datasets for industry to use toward certification of products,” said Vincent Paquit, head of the ORNL Secure and Digital Manufacturing section. “This is a data management platform structured to tell a complete story around an additively manufactured component. The goal is to use in-process measurements to predict the performance of the printed part.”

The 230 GB dataset covers the design, manufacturing and testing of five sets of parts with different geometric shapes, all made using a Laser Beam Powder Bed Fusion (PBF-LB) machine. Researchers can access machine health sensor data, laser scan paths, 30,000 powder bed images and 6,300 tests of the material’s tensile strength.

This is the fourth, and most extensive, in a series of AM datasets ORNL is making publicly available. Previous datasets have focused on the construction of parts made with Electron Beam Powder Bed Fusion and Binder Jetting at the MDF. The datasets can be searched for specific information needed to understand rare failure mechanisms, develop online analysis software or model material properties.

ORNL’s latest dataset is now available for free.