U.S. Navy Funds Senvol’s Machine Learning Software to Boost In-Situ Monitoring for 3D Printing

The additive manufacturing (AM) industry is well-positioned to benefit from increased incorporation of AI and machine learning (ML) into its core processes, and 2026 should provide plenty of opportunities for testing that potential. One factor to pay attention to while observing this trajectory is the quality of the data to which AI adopters in the AM industry have access.

Unsurprisingly, the U.S. Navy is ahead of the curve on this trend: the U.S. military branch has just awarded funding to information services firm Senvol for a project aiming to demonstrate that Senvol ML, the company’s ML software suite, can predict the mechanical performance of parts made with wire-based directed energy deposition (DED) AM. According to Senvol, the project, “[AM] Sensor Fusion Technologies for Process Monitoring and Control,” already began in July of last year, and will run through July 2027.

The research entails Senvol’s application of its software’s data analysis capabilities to the information gathered by different types of sensors already in use in the AM industry. Senvol hopes to help enable the Navy to ultimately implement in-situ monitoring standards across the branch’s constantly expanding AM activities.

In this context, wire DED is a logical place for the Navy to start, as the process drives the sort of hybrid systems that the branch has had installed on U.S. military vessels for a few years now. If the Navy can accelerate its standardization of DED parts with ML software, it can maximize the effectiveness of the systems already installed, while also supporting the case for a further ramp-up of onboard production capacity.

In a press release about Senvol’s U.S. Navy funding for its ML software, the president of Senvol, Zach Simkin, said, “Quality assurance in [AM] 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 analyzing in-situ monitoring data — and developing actionable guidance from it — will enable AM users to more readily meet part acceptance thresholds.”

Quality control is arguably the portion of the overall AM process where automation could make the biggest impact right now, and there are few AM users that could benefit from such an impact more than the U.S. Navy. The Maritime Industrial Base (MIB) is, of course, facing the most daunting workforce development challenge of virtually any area in the Western manufacturing landscape, so the Navy will welcome any solution that can reduce the need for labor without sacrificing standards.

Moreover, to return to the topic of onboard production capacity, this will always be an area where the need to minimize labor will be of the utmost priority, if only because there can only ever be a rather limited number of skilled workers on an active vessel at any given time. Meanwhile, the Navy has also repeatedly demonstrated that onboard production capacity is a central feature of its AM buildup, as opposed to just one use case among many.

Additionally, the U.S. military’s international partners, most namely the UK and Australia, have shown interest in cultivating the same capabilities. That contributes to making deployable maritime production capacity a priority, while also opening up the opportunity for even more comprehensive data-gathering to continue to refine the underlying tech involved.

If the U.S. Navy is able to standardize automated quality control for metal AM parts, the entire U.S. military stands to gain, not just the Navy. As I noted earlier this week, as each branch continues to raise its technical maturity, all the branches collectively are simultaneously increasing the viability of cross-branch AM cooperation.

Image courtesy of Senvol