Argonne’s advanced manufacturing proposal paves way for high-temperature, next-gen reactor components

March 17, 2026

Argonne research, conducted with multiple U.S. national laboratories, leads to proposed methods that will speed up material approval and expand applications of reactor materials, helping to make nuclear energy safer, more reliable, and economical

Argonne’s advanced manufacturing proposal paves way for high-temperature, next-gen reactor components image — Argonne researchers are using 3-D printing to investigate how to strengthen the nuclear supply chain
(Image by Argonne National Laboratory).

Argonne National Laboratory submitted the first draft of an American Society of Mechanical Engineers (ASME) Code Case. This proposal would permit the use of Laser Powder Bed Fusion (LPBF) additive manufacturing process for high-temperature reactor components.

Why is this important?

Using LPBF – a cutting-edge 3D printing technology– for high-performance materials is a major breakthrough for applied material science in the nuclear energy industry. It is a transformative step toward strengthening the nuclear supply chain, reducing manufacturing lead times and expanding design flexibility for critical high-temperature structural components.

How did they do it?

This milestone was made possible through the collaborative efforts of researchers from Argonne National Laboratory, Oak Ridge National Laboratory, Idaho National Laboratory, and Los Alamos National Laboratory, working under the U.S. Department of Energy’s Office of Nuclear Energy’s Advanced Materials and Manufacturing Technologies (AMMT) program.

The program explores advanced manufacturing techniques such as LPBF, Directed Energy Deposition (DED), and Powder Metallurgy Hot Isostatic Pressing (PM-HIP). These methods are central to developing innovative materials, optimizing manufacturing processes, and addressing challenges in nuclear energy applications.

Argonne researchers collaborated with those in the AMMT program to translate the national laboratory’s cutting-edge materials and manufacturing research into standards and regulatory pathways. This resulted in progress toward accelerated deployment of advanced reactor technologies.

Mark Messner, Xuan Zhang, and Yiren Chen spearheaded the development with contributions from colleagues across the national laboratory system. They used Argonne’s Additive Manufacturing Laboratory to perform their research.

What’s next?

Argonne will continue to explore methods for further accelerating the qualification process using machine learning techniques. Their objective is to supplement more conventional, empirical analysis methods typically used by ASME to correlate and extrapolate time-dependent material test data, while also advancing digital qualification approaches that integrate in situ process monitoring, advanced data analytics, and other AI tools.

Embracing artificial intelligence-powered techniques like machine learning is in keeping with the DOE’s recently announced Genesis Mission, which aims to connect supercomputers, data and national laboratories like Argonne to accelerate scientific discovery, energy innovation, and national security.