  Modern product design in the engineering domain is increasingly driven by
computational analysis including finite-element based simulation, computational
optimization, and modern data analysis techniques such as machine learning. To
apply these methods, suitable data representations for components under
development as well as for related design criteria have to be found. While a
component's geometry is typically represented by a polygon surface mesh, it is
often not clear how to parametrize critical design properties in order to
enable efficient computational analysis. In the present work, we propose a
novel methodology to obtain a parameterization of a component's plastic
deformation behavior under stress, which is an important design criterion in
many application domains, for example, when optimizing the crash behavior in
the automotive context. Existing parameterizations limit computational analysis
to relatively simple deformations and typically require extensive input by an
expert, making the design process time intensive and costly. Hence, we propose
a way to derive a compact descriptor of deformation behavior that is based on
spectral mesh processing and enables a low-dimensional representation of also
complex deformations.We demonstrate the descriptor's ability to represent
relevant deformation behavior by applying it in a nearest-neighbor search to
identify similar simulation results in a filtering task. The proposed
descriptor provides a novel approach to the parametrization of geometric
deformation behavior and enables the use of state-of-the-art data analysis
techniques such as machine learning to engineering tasks concerned with plastic
deformation behavior.
