Scientists and engineers at Lawrence Livermore National Laboratory (LLNL) have developed a camera-based inspection system that can monitor complex 3D-printed structures layer by layer, using AI and machine learning (ML) to measure tiny variations and potentially identify problems before a part ever leaves the printer. The approach could reduce the time and labor required to inspect components produced through direct ink writing.
Lawrence Livermore National Laboratory (LLNL) scientists and engineers, in conjunction with the Department of Energy (DOE) and National Nuclear Security Administration (NNSA), are increasingly looking to AI, robotics and automation to help accelerate advanced manufacturing, materials discovery and experimental science. The work is part of a broader push to move faster from concept to deployment in mission-relevant technologies.
Lawrence Livermore National Laboratory (LLNL) scientists and engineers have been selected to lead 10 Phase I projects under the U.S. Department of Energy’s (DOE’s) Genesis Mission, applying AI to challenges spanning high-performance computing (HPC), fusion energy, Earth systems science, materials discovery, biology, quantum technologies and fundamental physics. LLNL researchers also will contribute to 19 additional Genesis Mission projects led by partner institutions across the national laboratories, academia and industry.
Lawrence Livermore National Laboratory (LLNL) is contributing AI-enabled payload optimization and advanced modeling and simulation expertise to Aires Tide, a collaborative National Nuclear Security Administration (NNSA) demonstration exploring new ways to design flight test vehicles. Developed in collaboration with Sandia National Laboratories, Los Alamos National Laboratory (LANL) and the Kansas City National Security Campus, Aires Tide brings together expertise across design, manufacturing and flight testing in a single cross-enterprise effort. The project is also an early example of the Department of Energy’s (DOE) Genesis Mission in practice.
LLNL leaders, scientists and engineers joined national voices at the Special Competitive Studies Project’s (SCSP) AI+ Expo May 7-9 in Washington, D.C., highlighting how AI is reshaping science, security and energy innovation. The public Expo brought together government, industry, academic and Department of Energy (DOE) national laboratories for three days of sessions, demonstrations and exhibits focused on AI, national security and U.S. technological competitiveness.
Advanced manufacturing: AI-enabled on-machine inspection of 3D-printed parts
LLNL researchers developed an AI-enabled inspection system that uses layer-by-layer camera images to monitor the geometry of parts during direct ink write additive manufacturing. The approach accurately measures printed features in real time and was demonstrated on a production-scale component too large for conventional CT inspection, providing a foundation for future closed-loop manufacturing and quality control.
Mission-critical science: AI agents to manage complex computing workflows
LLNL researchers developed an agentic AI framework that can build, deploy, troubleshoot and optimize high-performance computing applications, including conducting scaling studies across different computing resources. By combining specialized AI agents with human guidance and validation, the work explores how agentic systems could reduce time spent on complex computational tasks while keeping experts involved in critical decisions.
Data analysis and visualization: AI co-scientists explore complex scientific data
Researchers developed an AI “co-scientist” framework that can analyze unfamiliar scientific datasets, plan visualization strategies and build interactive applications from high-level research goals. Tested on scientific visualization challenges spanning multiple disciplines, the system demonstrates how coordinated AI agents could help scientists move from complex data to useful visual analysis while preserving human oversight and validation.
