Senvol provides data to other companies in order to help them implement 3D printing into their workflows. Its massive searchable network, called the Senvol Database, is dedicated to searching 3D printers and materials for engineers who require machines with specific properties, companies carrying out market research, cost and supply chain analysis, and 3D printer and material research, and people simply hoping to purchase their own 3D printers and material. The cool company also offers Senvol ML, a data-driven machine learning software suite that companies can use to quickly characterize or qualify AM processes and materials.

Senvol has been focusing on military applications this past year – the US Navy is using Senvol ML, and the company even joined the National Armaments Consortium last month. Now, the company has been given a grant to continue its work. The National Institute of Standards and Technology (NIST), a non-regulatory agency of the United States Department of Commerce, announced that it has awarded Senvol the grant so it can use its machine learning software for 3D printing to set up Process-Structure-Property (PSP) relationships.

“The work in this project will demonstrate the power of a data-driven machine learning approach for additive manufacturing process understanding and material characterization,” said Yan Lu, Senior Research Scientist at NIST. “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 with a mission to promote industrial competitiveness and innovation. It organizes its activities into laboratory programs that include information technology, material and physical measurement, nanoscale science and technology, engineering, and neutron research. The grant that it just awarded to Senvol is for a project called “Continuous Learning for Additive Manufacturing Processes Through Advanced Data Analytics.”

With the grant, Senvol will work to demonstrate data analytics can be applied to 3D printing data in order to establish the aforementioned PSP relationships by using Senvol AM to conduct the analyses. The capabilities of the software suite that will used during this project include the following:

  • adaptive sampling
  • generative learning
  • model reliability
  • transfer learning
  • hybrid modeling (incorporating a physics-based model into Senvol ML’s framework

In addition, the company will parameterize microstructure data, in-situ monitoring data, and non-destructive testing (NDT) data so that they can all be added into the AM Material Database (AMMD) at NIST.

Senvol ML also includes a computer vision algorithm that that performs real-time analysis of in-situ monitoring data. The data that the software will analyze during this project will be from various NIST test studies, and from its AM Benchmark Test Series as well.

At the end of this project, Senvol and AMMD will be integrated in such a way that any data stored within NIST’s database will be able to, as the company put it, be “seamlessly analyzed by Senvol’s machine learning software.”

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