The US Department of Energy’s Oak Ridge National Laboratory has released a new dataset on additive manufacturing that industry and researchers can use to evaluate and improve the quality of 3D-printed components.
For a decade, ORNL’s Manufacturing Demonstration Facility (MDF) has been collecting data on the performance of 3D printers. This new data, which is available online for free, can be used to train machine learning models to improve quality assessment.
“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 extensive data sets include the design, printing and testing of various parts produced using a laser powder bed printing system. Researchers can access machine sensor data, laser scan paths and tensile strength tests. This data helps to understand rare failure mechanisms and to model material properties.
The researchers have shown that these datasets can be used to train machine learning algorithms to predict the performance of printed parts. This is crucial for industrial-scale additive manufacturing, as characterizing each part would be too costly.
“This is a key enabler to additive manufacturing at industry scale, because they can’t afford to characterize every piece,” Paquit said. “Using this data can help them capture the link between intent, manufacturing and outcomes.”
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