NIST Awards Grant to Senvol on Additive Manufacturing Data
August 22, 2018 | Categories: Member News
Senvol’s machine learning software for additive manufacturing will be used to establish Process-Structure-Property (PSP) relationships
The National Institute of Standards and Technology (NIST) announced that it has awarded a grant to Senvol for a project titled “Continuous Learning for Additive Manufacturing Processes Through Advanced Data Analytics.”
Senvol’s work will focus on demonstrating that data analytics can be applied to additive manufacturing (AM) data to establish Process-Structure-Property (PSP) relationships. Senvol ML, Senvol’s data-driven machine learning software for AM, will be used to conduct the analyses. The data to be analyzed will come from NIST’s various round robin test studies as well as from its AM Benchmark Test Series.
Senvol ML capabilities that will be utilized include model reliability, adaptive sampling, generative learning, hybrid modeling (the incorporation of a physics-based model into Senvol ML’s framework), and transfer learning. Additionally, Senvol will parameterize in-situ monitoring data, non-destructive testing (NDT) data, and microstructure data so that these types of data can be incorporated into NIST’s AM Material Database (AMMD). The project will culminate with an integration between Senvol ML and AMMD such that data stored within AMMD can be seamlessly analyzed by Senvol’s machine learning software.
Yan Lu, Senior Research Scientist at NIST, commented, “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.”
NIST is a physical sciences laboratory, and a non-regulatory agency of the United States Department of Commerce. Its mission is to promote innovation and industrial competitiveness. NIST’s activities are organized into laboratory programs that include nanoscale science and technology, engineering, information technology, neutron research, material measurement, and physical measurement.
To learn more about NIST, please visit its website.
To learn more about Senvol ML, Senvol’s data-driven machine learning software for AM, click here.
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Courtesy of Senvol