  Prior work has shown Convolutional Neural Networks (CNNs) trained on
surrogate Computer Aided Design (CAD) models are able to detect and classify
real-world artefacts from photographs. The applications of which support
twinning of digital and physical assets in design, including rapid extraction
of part geometry from model repositories, information search \& retrieval and
identifying components in the field for maintenance, repair, and recording. The
performance of CNNs in classification tasks have been shown dependent on
training data set size and number of classes. Where prior works have used
relatively small surrogate model data sets ($<100$ models), the question
remains as to the ability of a CNN to differentiate between models in
increasingly large model repositories. This paper presents a method for
generating synthetic image data sets from online CAD model repositories, and
further investigates the capacity of an off-the-shelf CNN architecture trained
on synthetic data to classify models as class size increases. 1,000 CAD models
were curated and processed to generate large scale surrogate data sets,
featuring model coverage at steps of 10$^{\circ}$, 30$^{\circ}$, 60$^{\circ}$,
and 120$^{\circ}$ degrees. The findings demonstrate the capability of computer
vision algorithms to classify artefacts in model repositories of up to 200,
beyond this point the CNN's performance is observed to deteriorate
significantly, limiting its present ability for automated twinning of physical
to digital artefacts. Although, a match is more often found in the top-5
results showing potential for information search and retrieval on large
repositories of surrogate models.
