COLLEGE STATION, Texas — Texas A&M University System researchers have submitted a diverse portfolio of proposals in response to the FY2027 Pantex Plant Directed Research & Development (PDRD) Research Consortium Request for Proposals, addressing mission-critical challenges in digital transformation, production operations, and workforce development.
The FY2027 PDRD Research Consortium solicitation sought innovative university-led research aligned with Pantex mission priorities across digital transformation, energetics, materials science, production throughput, tooling and fabrication, nonproliferation, and workforce training. The program anticipated approximately $2 million in FY2027 funding supporting 7–15 projects lasting one to three years.
Selected proposals span five strategic topic areas identified in the solicitation, combining expertise in artificial intelligence, materials engineering, human factors, immersive technologies, and software engineering.
Advancing AI for Software Assurance
One proposal, Agentic AI for Automated Test Case Generation and Execution, addresses the Digital Transformation need for AI-driven software testing. Led through the Texas A&M Center for Applied Technology by Dr. Keith Biggers, the project proposes evaluating agentic artificial intelligence systems capable of automatically generating, executing, and refining software test cases directly from source code.
The research would compare multiple AI models, investigate integration with Azure DevOps environments, and develop a prototype capable of supporting unit, integration, and regression testing. Researchers aim to determine whether AI-generated testing can improve software coverage while reducing manual engineering effort and establishing a roadmap for future adoption within Pantex software assurance activities.
Improving Reliability of Polyurethane Tooling
Responding to the Production Throughput topic on polyurethane useful life, the Texas A&M Engineering Experiment Station (TEES) proposal focuses on predicting the service life of polyurethane tooling exposed to varying environmental conditions.
The TEES research team led by Dr. Congrui Grace Jim and Dr. Amir Asadi, proposes combining accelerated aging experiments with physics-informed and data-driven modeling to quantify how temperature and humidity affect the performance of polyurethane components used throughout Pantex operations. The resulting predictive framework would enable maintenance personnel to transition from reactive replacement schedules to condition-informed lifecycle management, potentially reducing unnecessary tooling replacement while improving operational readiness.
Applying Human Factors to Reduce Operational Errors
Researchers from the Texas A&M Center for Worker Health submitted a proposal targeting the Training research area focused on reducing human-initiated operational errors.
The project would evaluate Pantex’s procedure-writing guidance using established human factors principles and the Next Generation Advanced Procedure (NGAP) framework. In addition, the team proposes reviewing five unclassified operating procedures to identify opportunities for improving clarity, visual hierarchy, level of detail, and overall usability. The effort is intended to strengthen procedural quality and reduce the likelihood of errors in high-consequence work environments. This project lead for this effort is Dr. Farzan Sasangohar.
Building Immersive Training for High-Hazard Operations
A fourth proposal from the Texas A&M University College of Performance, Visualization and Fine Arts LIVE Lab introduces the Adaptive Immersive Training Framework (AITF), an immersive virtual reality platform designed to improve workforce readiness for high-hazard operations.
The project, proposed by Dr. Aaron Thibault and Dr. Ralph Barbagallo, involves developing a reusable pipeline that converts real-world facilities into high-fidelity virtual training environments using LiDAR scanning, CAD models, and photogrammetry. As an initial demonstration, researchers would create a virtual Joint Test Assembly training module that enables technicians to rehearse complex procedures in realistic digital environments while capturing performance metrics and procedural errors. The framework is intended to provide a foundation for future virtual, augmented, and mixed reality training capabilities at Pantex.
Advancing Intelligent Metrology with WTAMU Collaboration
An additional proposal led in collaboration with West Texas A&M University addresses Pantex Project 21 by introducing a Cyber-Physical-Human System (CPHS) framework for intelligent metrology. Submitted by Dr. Benton Allen, Assistant Professor of Systems Engineering in the WTAMU College of Engineering, the project combines rapid Direct Scanning Laser Tracker (DSLT) technology with AI-guided Coordinate Measuring Machine (CMM) verification using a “Scan-First, Probe-Smart” approach. Rapid non-contact scanning identifies potential tolerance concerns, after which targeted high-precision CMM measurements validate critical features. The effort also includes DSLT validation, automated 360° scanning, generalizable validation methods, and workforce development through a metrology curriculum. This approach aims to reduce inspection time by approximately 40% while maintaining mission-critical accuracy and strengthening the regional talent pipeline across Texas A&M, West Texas A&M, and Amarillo College.
Supporting Pantex Mission Needs
Collectively, the A&M System submissions reflect the multidisciplinary capabilities of the System in support of national security research. The proposals emphasize technologies that can improve operational efficiency, strengthen workforce performance, accelerate digital transformation, and provide decision-makers with data-driven tools for future technology transition. These projects contribute to the broader objectives of the FY2027 PDRD Research Consortium by advancing applied research with clear pathways toward operational implementation, while also strengthening the strategic partnership and collaboration between the A&M System and the Pantex Plant.
