  Machine Learning (ML) is of increasing interest for modeling parametric
effects in manufacturing processes. But this approach is limited to established
processes for which a deep physics-based understanding has been developed over
time, since state-of-the-art approaches focus on reducing the experimental
and/or computational costs of generating the training data but ignore the
inherent and significant cost of developing qualitatively accurate
physics-based models for new processes . This paper proposes a transfer
learning based approach to address this issue, in which a ML model is trained
on a large amount of computationally inexpensive data from a physics-based
process model (source) and then fine-tuned on a smaller amount of costly
experimental data (target). The novelty lies in pushing the boundaries of the
qualitative accuracy demanded of the source model, which is assumed to be high
in the literature, and is the root of the high model development cost. Our
approach is evaluated for modeling the printed line width in Fused Filament
Fabrication. Despite extreme functional and quantitative inaccuracies in the
source our approach reduces the model development cost by years, experimental
cost by 56-76%, computational cost by orders of magnitude, and prediction error
by 16-24%.
