Senvol, a New York-based company dedicated to assisting in the adoption of AM technologies, has been awarded a grant from the National Institute of Standards and Technology (NIST). The grant will support its “Continuous Learning for Additive Manufacturing Processes Through Advanced Data Analytics” project.
Through the project, Senvol is seeking to demonstrate how data analytics can be used with AM data to establish Process-Structure-Property (PSP) relationships. To do this, the company will employ its Senvol ML data-driven machine learning software for AM to analyze data from NIST’s round robin test studies and its AM Benchmark Test Series.
More specifically, Senvol will utilize its software’s capabilities, including model reliability, adaptive sampling, generative learning, hybrid modeling and transfer learning. Yan Lu, Senior Research Scientist at NIST, explains:
“The work in this project will demonstrate the power of a data-driven machine learning approach for additive manufacturing process understanding and material characterization. Furthermore, Senvol will showcase hybrid modeling, whereby physics-based models and data-driven models are joined under a single framework.”
As part of its NIST-funded project, Senvol says it will parameterize in situ monitoring data, non-destructive testing (NDT) data and microstructure data so that they can be integrated into NIST’s AM Material Database (AMMD). Ultimately, the project will seek to integrate Senvol ML and AMMD so that any data stored in NIST’s AMMD can be “seamlessly analyzed” by Senvol’s software.
Earlier this year, Senvol announced it was developing data-driven machine learning AM software for the U.S. Navy for analyzing the relationships between AM processes and material performance. Even more recently, the company became a part of the National Armaments Consortium (NAC), the industry and academia arm of the Department of Defense Ordnance Technology Consortium (DOTC).
