@misc{d533bad2aa804c6e8c9c938a1d3f746f,

title = "A Boundary Topology Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing",

abstract = "Minimizing the mass in electric machines while maintaining superior performance has become a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization for light-weighting electric machines using traditional approaches typically explore a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods like cell-based density approaches suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary TO where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the IEA-15MW radial flux direct-drive wind turbine generator. The free-from boundary TO resulted in smooth and concise shapes that can be easily additively manufactured with upto 20-ton reduction in electrical steel mass.",

keywords = "additive manufacturing, Bezier curves, parametric design, topology optimization",

author = "Latha Sethuraman and Ganesh Vijayakumar",

year = "2022",

language = "American English",

series = "Presented at the 2022 MMM-Intermag Conference, 10-14 January 2022, New Orleans, Louisiana",

type = "Other",

}