Within the federal government and military supply chains, there has been a recent increase in the utilization and deployment of additive manufacturing (AM) techniques for critical systems, including material variation. This rise in federal AM adoption comes as no surprise, as these advanced and innovative manufacturing methods are widely known for shrinking waiting periods for new and replacement parts for critical assets while drastically saving money on federal and military budgets.
But there are some downsides that accompany these AM benefits. Though additively manufactured (AMed) parts and systems are transforming federal government and DIB supply chains for the better, AMed assets will almost always have material variations, such as different surface textures and other imperfections, that can have negative effects on the lifecycles of AMed assets.
The U.S. Army and the Federal Aviation Administration (FAA) recently awarded Auburn University’s National Center for Additive Manufacturing (NCAME) with grants specifically dedicated to the research on understanding the material variation and addressing the qualification/certification challenge of AMed assets.
To learn more about these grants, the effects material variation has on AMed government and military assets, and how NCAME is helping to solve these challenges for the Army and the FAA, the GovDesignHub sat down with Auburn University’s Dr. Nima Shamsaei.
Here is what he had to say:
GovDesignHub (GDH): What is Auburn University’s National Center for Additive Manufacturing Excellence (NCAME)? How did NCAME come about? What is its mission and what kinds of projects does the Center work on?
Dr. Nima Shamsaei: NCAME is a public-private partnership established through a co-operative agreement with NASA in 2017. NASA recognized NCAME as its Strategic Academic Partner for additive manufacturing (AM) and assigned NCAME to conduct leading-edge AM research, collaborate with industry, and support education and workforce development.
Soon after its inception and through an international competitive search, NCAME was selected as one of the founding partners of ASTM Additive Manufacturing Center of Excellence (AM CoE) to conduct research to help close the gap in developing standards and advance AM education and workforce development.
The center has four specific goals. First, to facilitate the exchange of ideas and information through structured public/private partnerships and by interacting with experts on a global scale. Second, to conduct fundamental as well as industry-relevant AM research to support technology advancement. Third, to perform high-impact AM standards development and the certification/qualification of AM products. And fourth, to innovate, promote and lead activities supportive of AM workforce development. At NCAME, we conduct research in a wide range of technology readiness levels (TRLs) from fundamental to applied to fill the gap for TRL 4 – 6 research as much as possible.
About 30 faculty members from four different colleges have been involved in NCAME’s research and educational activities. The research activities at NCAME are focused on five core strategic areas.
“NCAME currently has over 100 partners from various industries, government agencies, nonprofits, and academia.” — Dr. Nima Shamsaei
The first core strategic area is the structural integrity of AM materials: Fatigue & Fracture; Materials Characterizations; Mechanical Behavior of Materials; Microstructure-Property Relationships; Computational Materials Science; Multiscale Modeling; ICME; and Qualification/Certification.
The second focuses on laser-material interactions and thermal aspects of AM: Laser Synthesis and Processing; Laser Diagnostics; Additive Nano-Manufacturing; In-situ Process Monitoring; and Thermal Modeling of AM Processes.
The third strategic area encompasses data analytics: Artificial Intelligence/Machine Learning.
The fourth is on digital manufacturing and AM cyber/physical security: Digital Twin; Industry 4.0; Sabotage Attacks and Detection.
The fifth and final core strategic strategy centers on applications: Design for Biomedical; Combustion; Heat Exchanges; Nuclear & Thermal Systems; and more.
NCAME currently has over 100 partners from various industries, government agencies, nonprofits, and academia. The center currently operates ten metal additive manufacturing systems; three EOS M290, a Renishaw AM250, a Renishaw 500Q Flex, a 3D Systems ProX350, an OR-Laser Creator, an Optomec LENS 750, a Concept Laser M100, and an Open AM Machine.
The center is also equipped with powder characterization equipment, multiple uniaxial and multiaxial fatigue testing machines with heating chambers for high temperature static and fatigue testing up to 1500°C, multiple thermal characterization instruments, Optical and Scanning Electron Microscopes, X-ray CT, and more.
In addition to its extensive experimental equipment, NCAME has significant capabilities in multiscale mechanics of materials modeling. The simulation tools at the disposal of NCAME span from atomic scale up to continuum scale, which are well-suited to tackle the multi-length scale characteristics of additively manufactured materials.
GDH: It was announced earlier this year that NCAME was awarded two additive manufacturing research grants for the U.S. Army and the Federal Aviation Agency, which will focus on material variation. What is material variation, and what additive manufacturing gaps does it cause for the Army and the FAA?
Dr. Nima Shamsaei: Both projects are related to matters affecting the qualification and certification of AMed metallic materials/parts. AMed materials are prone to imperfections such as heterogeneous microstructure, volumetric defects, surface texture, and residual stresses.
The variations in such imperfections can result in some uncertainties in the performance of the product during service life. Such uncertainties need to be quantified and accounted for during the design and qualification/certification of the product, particularly for critical applications. Lack of such knowledge results in an extensive experimental process to generate statistically significant data to be able to calculate the reliability of the product with an accepted confidence level.
“Lack of such knowledge results in an extensive experimental process to generate statistically significant data to be able to calculate the reliability of the product with an accepted confidence level.” — Dr. Nima Shamsaei
This extensive process needs to be repeated for different materials and different AM machines. In addition, due to localized heat sources specific to the AM processes, the geometry of a part can often affect the thermal history, which ultimately influences the mentioned variabilities, further exacerbating the challenge of AM qualification and certification. In simple words, the generated data in the laboratory may or may not be representative of the part, depending on several factors, including the part geometry.
GDH: And what are the solutions that NCAME is working on for the Army and FAA to close material variation gaps?
Dr. Nima Shamsaei: We have been conducting systematic research to identify the sources of variability in the process, including the effect of key process variables’ drifts within tolerances that affect the material properties. In addition, build-to-build and machine-to-machine variabilities will be studied.
The next step will be to quantify such variabilities and determine their effects on the parts’ mechanical performance, specifically their fatigue behavior. We have been working on the concept of defect criticality (both volumetric and surface defects) on the fatigue performance of AMed materials and how we can predict the fatigue behavior by establishing structure-property relationships.
Specifically for the Army, we are also looking into the possibility of establishing an equivalency framework allowing for fabrication of the same quality part on multiple AM machines. The goal of the project is to address current challenges related to exhaustive data-driven qualification practices, transferability of the data, and correlating material property data to part performance for AM processes.
GDH: How will these solutions impact and improve combat readiness for the Army, as well as improve the reliability of 3D printed metal aircraft materials for the FAA?
Dr. Nima Shamsaei: First of all, these projects help understand and quantify the sources of variabilities. This is very important because they affect the design and, ultimately, the weight of the product.
“First of all, these projects help understand and quantify the sources of variabilities. This is very important because they affect the design and, ultimately, the weight of the product.” — Dr. Nima Shamsaei
Secondly, understanding how anomalies affect the part performance is key in safety-critical applications. It is very important to understand if an anomaly can affect the part’s reliability and durability, and if so, to what extent. Thirdly, by understanding the source and effect of these anomalies, one can design against them or find remedies to avoid them.
Finally, generating machine agnostic datasets by establishing defect-fatigue correlations not only addresses the challenge of the transferability of the data among various AM machines, but also helps establish material property data to part performance correlations. Simply, materials data generated in the laboratory using standard specimens can possibility be used to design components with different geometries.
GDH: Is there any other exciting news that NCAME would like to share? Any recent news or upcoming projects that you would like to highlight?
Dr. Nima Shamsaei: Yes, we have recently received two additional awards, one from the National Institute for Standards and Technology (NIST) for about $1M and one from the National Science Foundation (NSF) for $4M, both related to the qualification of AMed products.
Concerning the NIST award, we have teamed up with ASTM International and are working to establish a data-driven framework for the non-destructive qualification of AMed parts through computer vision and machine learning. The framework is based on the identification of critical defects and the prediction of fatigue performance with non-destructive evaluation (NDE) data.
The NSF project, which is in partnership with Southern University and Louisiana State University, is to enable prediction-based qualification for AMed products. We are also closely working with the newly formed ASTM Consortium on Materials Data and Standardization (CMDS) to transfer the generated knowledge and data to the AM industry. Lack of robust understanding of process-structure-property (PSP) relationships and thus a reliable qualification approach, the current AM part qualification efforts are component testing-heavy, time consuming, and account for more than 50% of production cost. Through this NSF-funded research, we envision to establish a rapid qualification framework for AMed parts that is based on a rich database of PSP relationships and prediction capabilities.
