Weighted unsupervised domain adaptation considering geometry features and engineering performance of 3D design data

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. The proposed method is the first to apply domain adaptation to deep learning-based engineering performance prediction to overcome the problem of insufficient industrial data. In addition, we present a source instance weighting method called the bi-weighting strategy to consider the geometry features and engineering performance of 3D design data and avoid negative transfer for accurate prediction. 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. 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 multisource domain knowledge and can efficiently replace conventional finite element analysis.