Excellence in research: reliable additive manufacturing of advanced metal alloys through AI-enabled real-time monitoring and process control

The National Science Foundation Office of Integrative Activities awarded Prairie View A&M University $1.220 million on September 1, 2026, under the Integrative Activities program (CFDA 47.083) to develop artificial intelligence-enabled real-time monitoring and process control for reliable additive manufacturing of advanced metal alloys.

The project integrates real-time sensing, artificial intelligence, and advanced manufacturing techniques to accelerate material design cycles for aerospace, energy systems, and national defense applications. Tungsten-tantalum alloys will serve as the demonstration material due to their potential for use in extreme environments and manufacturing complexity. Prairie View A&M will collaborate with Texas A&M University to conduct the research, which spans computing, materials science, and manufacturing disciplines. The project will engage undergraduate and graduate students in research, curriculum development, seminars, workshops, and professional training. Outreach activities will introduce high school students to advanced manufacturing, artificial intelligence, and data science through hands-on learning experiences.

The award obligates $1.220 million through August 31, 2029, with place of performance at Prairie View, Texas. The research is designed to strengthen research capacity at the institution while promoting materials innovation, advancing intelligent manufacturing technologies, enhancing economic competitiveness, supporting energy security, and advancing national defense capabilities.

Abstract

Developing advanced metal alloys for aerospace, energy systems, and national defense often requires lengthy cycles of material design, manufacturing, testing, and redesign. This project seeks to accelerate that process by enabling additive manufacturing systems to monitor material formation in real time and use artificial intelligence to improve manufacturing decisions during production. By integrating real-time sensing, artificial intelligence, and advanced manufacturing, the project aims to improve the quality, reliability, and efficiency of manufacturing advanced metal alloys for demanding engineering applications. Tungsten?tantalum alloys will serve as a challenging demonstration material because of their potential for use in extreme environments and the difficulty of producing them with consistent internal structures. The project will strengthen research capacity at prairie view a & m university through collaboration with texas a & m university while creating interdisciplinary research and education opportunities spanning computing, materials science, and manufacturing. Undergraduate and graduate students will participate in research, curriculum development, seminars, workshops, and professional training, and outreach activities will introduce high school students to advanced manufacturing, artificial intelligence, and data science through hands-on learning experiences that expand participation in science, technology, engineering, and mathematics. By accelerating materials innovation and advancing intelligent manufacturing technologies, the project will promote the progress of science, strengthen the advanced manufacturing workforce, enhance economic competitiveness, support energy security, and advance technologies important to the national defense. The technical objective is to develop an artificial intelligence-enabled framework for real-time monitoring and process control during electron beam powder bed fusion additive manufacturing of advanced metal alloys. Backscattered electron and x-RAY detectors will be integrated into an open-architecture manufacturing platform to monitor changes in material properties and microstructural evolution during fabrication. Tungsten?tantalum alloy samples produced under varied processing conditions will be characterized to establish relationships among manufacturing parameters, sensor measurements, alloy composition, and resulting microstructures. Artificial intelligence models will learn compact representations of complex microstructures, predict microstructural evolution from manufacturing and sensor data, quantify prediction uncertainty, and recommend processing strategies that achieve desired material properties. The initial implementation will employ bayesian neural networks to provide trustworthy, uncertainty-aware predictions, while the framework is designed to incorporate emerging artificial intelligence models as the research progresses. Sequence-learning models will identify adaptive scanning strategies for closed-loop process control. The resulting framework will be evaluated across multiple alloy compositions and component geometries and integrated with the manufacturing platform for real-time optimization. The project will establish a broadly applicable foundation for artificial intelligence-enabled monitoring and intelligent process control in additive manufacturing, while providing datasets, artificial intelligence models, and sensing methodologies that accelerate the reliable manufacturing of advanced metal alloys. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are planned for this award.