Rapid Qualification of Materials

The implementation of advanced materials into service oftens takes decades due to the large-scale testing requirements.  By using a combination of microstructure-based modeling, in situ experiments, digital manufacturing, and uncertainty quantification, we aim to rapidly qualify the use of advanced materials into applications.  Two case studies are given below: additive manufactured materials and fiber reinforced composites.

Additive Manufacturing:

Engineers have been in a euphoric state surrounding the potential complex designs enabled by additive manufacturing (AM), but before these components can be adopted into service, their structural integrity must be addressed.  Through the DARPA Open Manufacturing program, our group has worked to identify the unique microstructures produced via AM, specifically the voids, residual stress gradients, and rough surfaces, influence on fatigue behavior, in order to rapidly qualify the use of AM components into service.  The materials of interest are IN718 and Ti-6Al-4V produced via selective laser melting, and the project couples advanced fatigue modeling with in situ experiments to enable validation of the models including the associated uncertainty quantification.  As part of the technology transition plan, the current model-based framework is being used to rapidly qualify AM components.

Discontinous Fiber Reinforced Composites:

Our team has worked to develop predictive engineering tools for discontinuous fiber composites.  In one example, our team led the validation efforts, which ultimately resulted in software development that was deployed in commercial tools for fiber attrition during injection molding of long-fiber reinforced thermoplastics.  The model predictions of the composite properties were within 15% of the measured values.  Therefore, development of this tool to introduce long fiber thermoplastic composites resulted in a 35% weight savings for a demonstrated vehicle system, and therefore substantial cost savings in energy efficiency.  In a second example, we used a combination of modeling and in situ experiments at the microstructural length scale to predict the strength and progressive damage properties of short fiber reinforced composites (the fiber architecture is pictured on the right).


© msangid 2022