Towards Simplifying Electrical Machine Design with Automated CAD Component Identification using Mask RCNN

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Abstract
The state-of-the-art design processes for electrical machines (eMachine) are template-based methods and design from scratch approaches. Over the past four decades, significant advancements have been made to make these methods effective in terms of electromagnetic performance. However, the state-of-the-art cannot provide information on the topology and the components of an eMachine from a given non-parametrized computer aided design (CAD). This information is imperative for the automatic selection of an appropriate simulation profile to meet the required key performance indicators (KPIs), enhancing the numerical computation of electromagnetic fields, and improving the user experience through an improved user interface. Moreover, it can be used to generate a parametric version of a given CAD design, which can subsequently be utilized by an optimization tool for geometric refinement and optimal material selection. The first step to achieve this is recognizing the components and their relative special positioning. This paper uses a Mask Region-based Convolutional Neural Network (Mask RCNN), an object detection technique, to identify the components of an eMachine from a CAD design.
Funding Information
  • Mitacs
  • Natural Sciences and Engineering Research Council of Canada (RGPIN-2020-05126)

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