Senvol Receives Funding from U.S. Navy and U.S. Air Force for Software
Data-driven machine learning software analyzes the relationships between additive manufacturing process parameters and material performance, company says.
Engineering Computing News
Engineering Computing Resources
March 8, 2021
Senvol has received further funding from the U.S. Navy and U.S. Air Force to develop additional capabilities for its additive manufacturing (AM) machine learning software, Senvol ML. Funding from the U.S. Navy is being provided by the Office of Naval Research (ONR), Naval Sea Systems Command (NAVSEA) and Naval Air Systems Command (NAVAIR). Funding from the U.S. Air Force is being provided by the Air Force Research Laboratory (AFRL).
Senvol’s AM machine learning software, Senvol ML, can be used to analyze data from any AM process, any AM machine and any AM material. The Senvol ML software currently contains capabilities that can be used to:
- rapidly optimize AM process parameters;
- support qualification of AM machines and materials;
- predict material properties;
- gain insights from in-situ monitoring data to support quality assurance; and
- minimize data generation costs.
The Senvol ML software was made commercially available in November 2019, coinciding with the completion of Senvol’s Phase II Base STTR effort with ONR. The new funding, exercised in support of the Phase II Option, is going toward the development of new capabilities that will be rolled into the Senvol ML software.
“Our collective objective is to enable organizations to quickly characterize or qualify additive manufacturing materials and processes,” says Senvol President Annie Wang. “The new capabilities that we are developing are quite compelling and will augment the Senvol ML software’s existing suite of capabilities.”
Users of Senvol ML include organizations in aerospace, defense, oil & gas, consumer products, medical and automotive industries, as well as AM machine manufacturers and AM material suppliers.
To learn more about Senvol ML, Senvol’s data-driven machine learning software for AM, click here.
Sources: Press materials received from the company and additional information gleaned from the company’s website.
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