  The product design process in manufacturing involves iterative design
modeling and analysis to achieve the target engineering performance, but such
an iterative process is time consuming and computationally expensive. Recently,
deep learning-based engineering performance prediction models have been
proposed to accelerate design optimization. However, they only guarantee
predictions on training data and may be inaccurate when applied to new domain
data. In particular, 3D design data have complex features, which means domains
with various distributions exist. Thus, the utilization of deep learning has
limitations due to the heavy data collection and training burdens. We propose a
bi-weighted unsupervised domain adaptation approach that considers the geometry
features and engineering performance of 3D design data. It is specialized for
deep learning-based engineering performance predictions. Domain-invariant
features can be extracted through an adversarial training strategy by using
hypothesis discrepancy, and a multi-output regression task can be performed
with the extracted features to predict the engineering performance. In
particular, we present a source instance weighting method suitable for 3D
design data to avoid negative transfers. The developed bi-weighting strategy
based on the geometry features and engineering performance of engineering
structures is incorporated into the training process. The proposed model is
tested on a wheel impact analysis problem to predict the magnitude of the
maximum von Mises stress and the corresponding location of 3D road wheels. This
mechanism can reduce the target risk for unlabeled target domains on the basis
of weighted multi-source domain knowledge and can efficiently replace
conventional finite element analysis.
