Automatic feature recognition from imperfect models using a novel workflow of data surrogation

ABSTRACT:

Imperfect CAD models with non-smooth features are common outputs of the latest digital tools. These are unsuitable for the feature recognition needed for end applications like computer-aided manufacturing. This paper proposes to recognise features from imperfect models by contributing a comprehensive dataset, a novel data surrogation method, and ML-based automated feature recognition model. Results show that the data surrogation method accurately replicates manual imperfections with voxel accuracy >0.9 and a Dice coefficient >0.6. Ultimately, feature recognition achieves 92.8% test accuracy.