Speaker
Description
Metallic porous materials are used in wide-ranging practical applications, from lightweight metal structures to medical implants. However, their use in a given application depends on obtaining the desired mechanical properties, such as Young’s modulus and yield stress. Advances in 3D printing enable the creating a wider choice of prescribed structures than ever before. However, to take advantage of this opportunity one must predict which structures will exhibit the desired mechanical properties. Instead of testing each candidate structure directly using the computationally expensive simulation methods, our strategy rests on using Fast Fourier Transform-based computational methods to create a database of sample structures that will be used to build predictive models based on variables defined by topological data analysis (TDA), whose results can be extrapolated to a wider space of possible structures.
