Senvol, creator of the world’s largest additive manufacturing (AM) database, has signed a two-year research agreement with the U.S. Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL) focused on AM data generation.
Using Senvol’s proprietary Standard Operating Procedure (SOP) document which covers collecting appropriate geometric information, key processing parameters for the AM technology, and key material testing protocols, the project will investigate and evaluate best practices for pedigreed data collection for the quality of AM materials, ensuring all required nuanced data is captured and accurately extracted during an AM data generation project.
“Senvol has been at the forefront of pedigreed data for additive manufacturing,” Ryan Dehoff, group leader of the Deposition Sciences and Technology Group at ORNL, explained. “The importance of understanding the relationship between material properties, machine selection, and process parameters is critical for helping industry move from prototypes to industrial parts.”
Senvol President Annie Wang commented, “Oak Ridge is renowned for having world-class expertise in additive manufacturing, and so we’re very excited to work with them on this project. The pedigreed data generated during the project will be input into Senvol’s data structure in order to perform preliminary machine learning and data analysis. Senvol is currently evaluating and building advanced computational tools to rapidly evaluate AM components and link processing, microstructure, and properties in additively manufactured components. The results of this project will be used to complement physics based models of additive manufacturing systems and therefore lead to more rapid understanding of new materials and faster deployment of the technology.”
The research is supported by DOE’s Office of Energy Efficiency and Renewable Energy-Advanced Manufacturing Office under the Manufacturing Demonstration Facility at ORNL which supports early stage applied research and development of new materials, information, and processes that improve American manufacturing’s energy efficiency.
