  Considering data insufficiency in metal additive manufacturing (AM), transfer
learning (TL) has been adopted to extract knowledge from source domains (e.g.,
completed printings) to improve the modeling performance in target domains
(e.g., new printings). Current applications use all accessible source data
directly in TL with no regard to the similarity between source and target data.
This paper proposes a systematic method to find appropriate subsets of source
data based on similarities between the source and target datasets for a given
set of limited target domain data. Such similarity is characterized by the
spatial and model distance metrics. A Pareto frontier-based source data
selection method is developed, where the source data located on the Pareto
frontier defined by two similarity distance metrics are selected iteratively.
The method is integrated into an instance-based TL method (decision tree
regression model) and a model-based TL method (fine-tuned artificial neural
network). Both models are then tested on several regression tasks in metal AM.
Comparison results demonstrate that 1) the source data selection method is
general and supports integration with various TL methods and distance metrics,
2) compared with using all source data, the proposed method can find a small
subset of source data from the same domain with better TL performance in metal
AM regression tasks involving different processes and machines, and 3) when
multiple source domains exist, the source data selection method could find the
subset from one source domain to obtain comparable or better TL performance
than the model constructed using data from all source domains.
