Researchers at Lawrence Livermore National Laboratory (LLNL) are breaking new ground in non-destructive evaluation (NDE) for metal additive manufacturing (AM), developing techniques to monitor structures in real time as they are 3D printed. These approaches aim to enhance part quality, consistency, and reliability for applications in aerospace, energy, and other safety-critical sectors.
David Stobbe, group leader for NDE ultrasonics and sensors in LLNL’s Materials Engineering Division (MED), said, “If you want people to use metal AM components out in the world, you need NDE. If we can prove that AM-produced parts behave as designed, it will allow them to proliferate, be used in safety-critical components, and hopefully open a new paradigm in manufacturing.”
The team is using innovative NDE methods such as eddy current sensors to monitor subsurface temperatures during laser powder-bed fusion, electrical resistance tomography, high-speed synchrotron X-ray imaging, ultrasound, and neutron detection. These techniques allow researchers to detect defects, track material evolution, and optimize processing parameters without destroying the part.
Saptarshi Mukherjee, research scientist at LLNL, explained, “Evolving processes in the subsurface need to be measured and characterized if you want to have a consistent print quality. This is very challenging because most of the current NDE technologies cannot see through heat, and even infrared cameras only detect surface effects.”
By integrating NDE with advanced simulations and machine learning, LLNL scientists aim to provide manufacturers with real-time feedback during printing, enabling error correction and predictive control. This approach could accelerate adoption of metal AM, making high-performance components safer and more reliable.
Ethan Rosenberg, MED postdoc, added, “A lot of things happen in these AM processes that affect the part, but without using NDE techniques, it's kind of a black box. With ingenuity and good physical understanding, you can open that box to see what's happening inside, and that will hopefully help you control the process.”
The LLNL team plans to continue developing a suite of NDE methods for metal AM, ensuring manufacturers can reliably produce complex metal components and leverage the full potential of additive manufacturing technologies.
Reference: https://techxplore.com/news/2025-09-advanced-sensors-peer-black-metal.html
