8/29/2018 | 1 MINUTE READ

Senvol Recieves NIST Grant for AM Data

Senvol’s machine learning software for additive manufacturing will be used to establish Process-Structure-Property (PSP) relationships.


Facebook Share Icon LinkedIn Share Icon Twitter Share Icon Share by EMail icon Print Icon

Senvol has received a grant from the National Institute of Standards and Technology (NIST) 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, comments, “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.”