Senvol to lead US Navy Additive Manufacturing qualification study
Senvol, located in New York City, USA, has received funding from the US Navy to lead a project focused on demonstrating that Senvol ML, the company’s machine learning software, can accurately predict the material performance of parts made on a metal wire Directed Energy Deposition (DED) Additive Manufacturing machine.
The goal of the project, titled “Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control,” is to implement a standardised procedure for assessing AM parts for quality acceptance and installation using data-driven machine learning algorithms that provide insights needed to achieve target mechanical performance requirements. Senvol will utilise its AM machine learning software, Senvol ML, to analyse in-situ monitoring data from various sensor types and modalities.
For AM to be successfully implemented into the Navy’s supply chain, it is essential to be able to ensure quality and develop sufficient evidence to support the acceptance of an AM part for installation. The solution proposed by Senvol under this project would allow the Navy to implement a standardised procedure for assessing AM parts for quality acceptance and installation using data-driven machine learning algorithms that provide insights needed to achieve target mechanical performance requirements.
The approach demonstrated in the project will help progress the Navy toward achieving qualified, equivalent AM parts from a more flexible and scalable AM supply base, both organic and commercial, without the need for costly and time-consuming qualification and testing. Furthermore, the solution offered will help the Navy address that need and enable the use of in-situ monitoring data for part acceptance by integrating in-situ monitoring requirements into NAVSEA policy.
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During the project, Senvol will use Senvol ML to parameterise the data collected from the in-situ monitoring sensors and compute summary features linked to phenomena deemed valuable for analysis. The objective is for Senvol’s machine learning software to accurately predict material performance characteristics from in-situ monitoring data, as well as to choose process parameters likely to produce parts with the desired characteristics.
Senvol President Zach Simkin commented, “Quality assurance in Additive Manufacturing is critical. For a part to be accepted into the supply chain, there needs to be sufficient confidence regarding how the part will perform. Progress in this area continues to evolve, and we believe that developing a consistent approach to analysing in-situ monitoring data – and developing actionable guidance from it – will enable AM users to more readily meet part acceptance thresholds.”
