  Technological advances are in part enabled by the development of novel
manufacturing processes that give rise to new materials or material property
improvements. Development and evaluation of new manufacturing methodologies is
labor-, time-, and resource-intensive expensive due to complex, poorly defined
relationships between advanced manufacturing process parameters and the
resulting microstructures. In this work, we present a topological
representation of temper (heat-treatment) dependent material micro-structure,
as captured by scanning electron microscopy, called TopTemp. We show that this
topological representation is able to support temper classification of
microstructures in a data limited setting, generalizes well to previously
unseen samples, is robust to image perturbations, and captures domain
interpretable features. The presented work outperforms conventional deep
learning baselines and is a first step towards improving understanding of
process parameters and resulting material properties.
