ORNL’s Additive Manufacturing datasets now available to evaluate the quality of additively manufactured components
July 22, 2024
The US Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL), based in Tennessee, USA, has released a new set of Additive Manufacturing data that industry and researchers can use to evaluate and improve the quality of additively manufactured components. The breadth of the datasets can significantly boost efforts to verify the quality of the parts using only information gathered during Additive Manufacturing, without requiring expensive and time-consuming post-production analysis.
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 Additive Manufacturing machines perform.
The conventional manufacturing industry benefits from centuries of quality-control experience. However, Additive Manufacturing 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 Additive Manufacturing datasets 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 reportedly the most extensive, in a series of Additive Manufacturing datasets ORNL is making publicly available. Previous datasets have focused on the construction of parts made with Electron Beam Powder Bed Fusion (PBF-EB) and Binder Jetting (BJT) 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.
The MDF, supported by DOE’s Advanced Materials and Manufacturing Technologies Office, is a nationwide consortium of collaborators working with ORNL to catalyse the transformation of US manufacturing.
ORNL researchers demonstrated how to apply the datasets by training a machine learning algorithm using measurements taken during the Additive Manufacturing process. Paired with high-performance computing methods, the trained algorithm can reliably predict whether a mechanical test will be successful. It also made 61% fewer errors in predicting a part’s ultimate tensile strength.
“Correlating in-process measurements with the final product is key to providing confidence about when an additional test of the part is needed – and when it’s not. This is a key enabler to Additive Manufacturing at industry scale, because they can’t afford to characterise every piece,” Paquit shared. “Using this data can help them capture the link between intent, manufacturing and outcomes.”
The data generated was part of the Advanced Materials and Manufacturing Technology Program, funded by DOE’s Office of Nuclear Energy. These and other smart manufacturing approaches are being used to accelerate the development, qualification, demonstration and deployment of advanced manufacturing technologies to enable reliable and economical nuclear energy.
ORNL’s latest dataset is now available for free.
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