Dr. Meimei Li
Argonne National Laboratory
Presenting in Track 16: Special Symposium on Additive Manufacturing Benchmark Test Series Symposium
Presentation Title: Accelerated Qualification of Additively Manufactured Materials for Nuclear Energy Applications
Abstract: The advancement of nuclear energy technologies necessitates the development and qualification of materials capable of withstanding extreme conditions such as high temperatures, stress, radiation, and corrosive environments. Traditional material qualification processes are often time-consuming and costly, creating significant barriers to the rapid deployment of innovative materials and manufacturing techniques. Additively manufactured (AM) materials offer transformative potential for the nuclear energy sector by enabling enhanced design flexibility, improved performance, and reduced costs. However, qualifying these materials for nuclear applications presents significant challenges due to their unique characteristics and stringent requirements for safety, reliability, and long-term performance of reactor structural materials. This presentation discusses an accelerated qualification framework for AM materials in nuclear energy systems.
This accelerated qualification framework integrates scientific understanding with engineering data, using predictive modeling and machine learning/artificial intelligence (ML/AI) tools to enhance the prediction of material behavior and performance limits in nuclear environments. Central to this approach is the establishment of robust processing-structure-property-performance (P-S-P-P) relationships, which are crucial for predicting the long-term behavior of additively manufactured (AM) materials in nuclear environments. By understanding how manufacturing processes influence microstructure and, consequently, material properties and performance, predictive models can be developed to inform design decisions and optimize performance. The approach utilizes high-throughput testing and advanced characterization techniques to rapidly generate comprehensive datasets that are essential for understanding the complex interactions between material composition, processing conditions, and environmental factors. In-situ process monitoring provides real-time insights into the quality and integrity of fabricated parts, improving quality control. Combined with AI/ML tools, this data accelerates the assessment and optimization of material properties, supporting a more efficient and reliable qualification process. Computational tools complement experimental data by providing predictive insights into the P-S-P-P relationships and extrapolating long-term behavior. Sensitivity analysis and uncertainty quantification are employed to understand the underlying physics and reliably predict material performance beyond testing conditions.
A case study on laser powder bed fusion (LPBF) 316H stainless steel illustrates the framework's potential to expedite the qualification process for high-temperature nuclear structural applications. We will highlight a benchmark study focused on predicting creep properties of additively manufactured components with a simple test geometry. The potential for digital qualification of AM components for use in nuclear reactors employing the Multi-Dimensional Data Correlation (MDDC) platform, leveraging digital twins, manufacturing data, process monitoring, machine learning, data analytics, Non- Destructive Evaluation (NDE), and integration into codes and standards will also be discussed, along with the approach combining neutron and ion irradiations with physics-based modeling for accelerated material qualification for radiation effects and evaluations of corrosion effects in various reactor environments, focusing on the unique characteristics of additively manufactured materials.
Biography: Dr. Meimei Li is a materials scientist in the Nuclear Science and Engineering Division, Argonne National Laboratory. She leads the DOE Office of Nuclear Energy Advanced Materials and Manufacturing Technologies (AMMT) program as the National Technical Director (NTD). She has broad research experience including radiation damage, corrosion, alloy development, testing, characterization qualification, and advanced manufacturing in support of a broad range of nuclear reactor technologies. Prior to joining the ANL, she worked at the Materials Science and Technology Division at the Oak Ridge National Laboratory.