Lawrence Livermore National Laboratory develops AI-powered 3D printing inspection system

Scientists and engineers at Lawrence Livermore National Laboratory (LLNL) have developed a camera-based inspection system designed to monitor complex 3D-printed structures layer by layer. By utilizing artificial intelligence and machine learning, the system can measure minute variations and identify potential defects during the printing process, rather than after completion.

The technology specifically targets direct ink writing, an additive manufacturing technique that uses a nozzle to deposit soft or paste-like materials in thin strands. This method is often used to create intricate, flexible components like cushions or pads, where even tiny gaps or changes in filament diameter can compromise mechanical performance.

According to researchers, this AI-driven approach offers a more efficient alternative to traditional offline inspection methods, such as X-ray computed tomography (CT), which can be expensive, time-consuming, and difficult to apply to large or highly complex parts. The system uses computer vision to convert thousands of captured images into detailed spatial maps and measurements, allowing for data-informed decision-making during production.