  Advanced manufacturing techniques have enabled the production of materials
with state-of-the-art properties. In many cases however, the development of
physics-based models of these techniques lags behind their use in the lab. This
means that designing and running experiments proceeds largely via trial and
error. This is sub-optimal since experiments are cost-, time-, and
labor-intensive. In this work we propose a machine learning framework,
differential property classification (DPC), which enables an experimenter to
leverage machine learning's unparalleled pattern matching capability to pursue
data-driven experimental design. DPC takes two possible experiment parameter
sets and outputs a prediction of which will produce a material with a more
desirable property specified by the operator. We demonstrate the success of DPC
on AA7075 tube manufacturing process and mechanical property data using shear
assisted processing and extrusion (ShAPE), a solid phase processing technology.
We show that by focusing on the experimenter's need to choose between multiple
candidate experimental parameters, we can reframe the challenging regression
task of predicting material properties from processing parameters, into a
classification task on which machine learning models can achieve good
performance.
