  The design of an electrical machine can be quantified and evaluated by Key
Performance Indicators (KPIs) such as maximum torque, critical field strength,
costs of active parts, sound power, etc. Generally, cross-domain tool-chains
are used to optimize all the KPIs from different domains (multi-objective
optimization) by varying the given input parameters in the largest possible
design space. This optimization process involves magneto-static finite element
simulation to obtain these decisive KPIs. It makes the whole process a
vehemently time-consuming computational task that counts on the availability of
resources with the involvement of high computational cost. In this paper, a
data-aided, deep learning-based meta-model is employed to predict the KPIs of
an electrical machine quickly and with high accuracy to accelerate the full
optimization process and reduce its computational costs. The focus is on
analyzing various forms of input data that serve as a geometry representation
of the machine. Namely, these are the cross-section image of the electrical
machine that allows a very general description of the geometry relating to
different topologies and the classical way with scalar parametrization of
geometry. The impact of the resolution of the image is studied in detail. The
results show a high prediction accuracy and proof that the validity of a deep
learning-based meta-model to minimize the optimization time. The results also
indicate that the prediction quality of an image-based approach can be made
comparable to the classical way based on scalar parameters.
