NIAR develops advanced composite materials for the U.S. Air Force

NIAR develops advanced composite materials for the U.S. Air Force

The National Institute of Aeronautical Sciences (NIAR) continues its Affordable and Sustainable Composite Modeling (MASC) research program in November 2020.

MASC program the first phase of the main purpose is to develop the certification system, and for automated manufacturing pave the way for the advanced materials and structure of the concept, and a new round of funding will allow researchers to further study on the damage failure behavior of materials, and the identification and characterization of the defense industry of strategic importance material system, including the use of automation manufacture process of ceramic matrix composites, and the compression foam thermoplastic composite materials and injection molding composite materials.

NIAR's Aerospace Systems Advanced Technology Laboratory (ATLAS) leads the MASC program in collaboration with researchers, defense technology contractors, and related universities. The program will leverage automation, machine learning and artificial intelligence algorithms to enhance tools used to build the next generation of advanced integrated composite body structures.

According to the researchers, the goal of the MASC program fits well with ATLAS's mission -- to create a multidisciplinary manufacturing environment and engineering education program that will prepare future factory engineers and educators. That is expected to happen as researchers develop technologies of strategic importance to the Pentagon and the aerospace industry.

In its first year, ATLAS has hired more than 50 engineering students. The work at ATLAS has provided students with valuable experience and prepared them to work in an engineering environment. ATLAS provides students with the opportunity to apply what they have learned in the classroom to practice, creating and designing composite materials.

The new round of funding will enable NIAR researchers to develop technology to optimize digital twin data for analyzing material damage and defects, and to automate analytical maintenance processes using high-fidelity analysis techniques. Machine learning algorithms suitable for the concept of digital twinning will be developed to cross-examine defect profiles for each part, identify various component styles and application patterns, and optimize processes. This data information can be used to improve part quality, tool design, and manufacturing speed.

Through the program, researchers will use advanced technologies developed through the MASC to manufacture full-scale prototypes and assess the affordability, producibility and maintainability of components compared to traditional manufacturing techniques.