Comparison of Transfer Learning based Additive Manufacturing Models via
  A Case Study
Yifan Tang M. Rahmani Dehaghani G. Gary Wang
Transfer learning (TL) based additive manufacturing (AM) modeling is an
emerging field to reuse the data from historical products and mitigate the data
insufficiency in modeling new products. Although some trials have been
conducted recently, the inherent challenges of applying TL in AM modeling are
seldom discussed, e.g., which source domain to use, how much target data is
needed, and whether to apply data preprocessing techniques. This paper aims to
answer those questions through a case study defined based on an open-source
dataset about metal AM products. In the case study, five TL methods are
integrated with decision tree regression (DTR) and artificial neural network
(ANN) to construct six TL-based models, whose performances are then compared
with the baseline DTR and ANN in a proposed validation framework. The
comparisons are used to quantify the performance of applied TL methods and are
discussed from the perspective of similarity, training data size, and data
preprocessing. Finally, the source AM domain with larger qualitative similarity
and a certain range of target-to-source training data size ratio are
recommended. Besides, the data preprocessing should be performed carefully to
balance the modeling performance and the performance improvement due to TL.