  Additive Manufacturing presents a great application area for Machine Learning
because of the vast volume of data generated and the potential to mine this
data to control outcomes. In this paper we present preliminary work on
classifying infrared time-series data representing melt-pool temperature in a
metal 3D printing process. Our ultimate objective is to use this data to
predict process outcomes (e.g. hardness, porosity, surface roughness). In the
work presented here we simply show that there is a signal in this data that can
be used for the classification of different components and stages of the AM
process. In line with other Machine Learning research on time-series
classification we use k-Nearest Neighbour classifiers. The results we present
suggests that Dynamic Time Warping is an effective distance measure compared
with alternatives for 3D printing data of this type.
