Senvol, a company that develops software for analyzing additive manufacturing process and material data, has received funding from the U.S. Navy to lead a research program focused on sensor fusion in metal additive manufacturing. The effort will examine whether in-situ monitoring data can be used to predict material performance for parts produced using metal wire directed energy deposition systems, supporting quality acceptance and installation decisions.
The project, titled Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control, began in July 2025 and is scheduled to run through July 2027. Work under the program will evaluate whether machine learning methods can analyze data collected during manufacturing to determine part performance characteristics directly from process monitoring information.
The software developer will apply its machine learning software, Senvol ML, to in-situ monitoring data generated during metal wire directed energy deposition builds. The software platform is designed to process data from multiple sensor types and modalities by parameterizing the collected information and computing summary features associated with manufacturing phenomena identified as relevant to material behavior.
Program objectives focus on establishing a standardized, data-driven procedure for assessing additive manufacturing part quality and suitability for installation. Machine learning models will be used to link in-situ monitoring data to mechanical performance requirements, providing quantitative evidence intended to support acceptance decisions without relying exclusively on extensive post-build qualification and testing.
For Navy supply chains, adoption of additive manufacturing depends on the ability to ensure consistent quality and predictable performance. The approach under evaluation is intended to generate sufficient confidence in additively manufactured parts to allow their acceptance for installation, addressing a key barrier to broader use of additive manufacturing in sustainment and production contexts.
The methodology demonstrated under the program is also intended to support a more flexible and scalable additive manufacturing supply base. By reducing dependence on repeated qualification campaigns, the project seeks to enable production of equivalent parts across both organic Navy facilities and commercial suppliers. This approach could allow qualified components to be sourced from a wider range of additive manufacturing providers while maintaining required performance characteristics.
Integration of in-situ monitoring data into formal acceptance workflows is another focus of the effort. Project outcomes are expected to inform how monitoring requirements could be incorporated into policy frameworks administered by NAVSEA, enabling process data collected during manufacturing to be considered as part of acceptance criteria.
During the program, Senvol’s software will also be used to evaluate how process parameters influence resulting material properties. In addition to predicting performance characteristics from monitoring data, the system is intended to identify parameter combinations likely to produce parts with targeted mechanical attributes.
Commenting on the project, Senvol President Zach Simkin said, “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 analyzing in-situ monitoring data – and developing actionable guidance from it – will enable AM users to more readily meet part acceptance thresholds.”
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Featured image shows An example analysis from Senvol ML – stainless steel powder and laser powder bed fusion. Image via Senvol.
