Senvol to demonstrate that the company's machine learning software can accurately predict the material performance of parts produced with metal wire directed energy deposition equipment. american navy.
The goal of the project, titled “Additive Manufacturing Sensor Fusion Technology for Process Monitoring and Control,” is to implement a standardized procedure for assessing quality acceptance and installation of additively manufactured parts using data-driven machine learning algorithms that “provide the insights needed to achieve target mechanical performance requirements.”
Since July 2025, Senvol has utilized Senvol ML software to analyze field monitoring data from various sensor types and various modalities. The project will run through July 2027 and is expected to help the Navy move toward achieving qualified equivalent AM parts from a more flexible and scalable AM supply base. Through this initiative, the U.S. Navy hopes to reduce the need for costly and time-consuming certification and testing while integrating field surveillance requirements into NAVSEA policy.
During the project, Senvol will use Senvol ML to parameterize data collected from field monitoring sensors and calculate summary features related to specific phenomena that are considered worth collecting information about. The goal of Senvol's machine learning software is to accurately predict material performance properties from field monitoring data and select process parameters that are likely to produce parts with desired properties.
“Quality assurance in additive manufacturing is critical. For a part to be accepted into the supply chain, there must be sufficient confidence in how it will perform,” said Zach Simkin, president of Senvol. “Advances in this field continue to evolve, and we believe that by developing a consistent approach to analyzing field monitoring data, and from there practical guidance, we will make it easier for AM users to meet part acceptance criteria.”
