  Transforming a design into a high-quality product is a challenge in metal
additive manufacturing due to rare events which can cause defects to form.
Detecting these events in-situ could, however, reduce inspection costs, enable
corrective action, and is the first step towards a future of tailored material
properties. In this study a model is trained on laser input information to
predict nominal laser melting conditions. An anomaly score is then calculated
by taking the difference between the predictions and new observations. The
model is evaluated on a dataset with known defects achieving an F1 score of
0.821. This study shows that anomaly detection methods are an important tool in
developing robust defect detection methods.
