Work

Multi-Objective Topology Optimization of Rotating Machines Using Deep Learning

Year: 2019

Type: article

Abstract: This paper presents the fast topology optimization methods for rotating machines based on deep learning. The cross-sectional image of electric motors and their performances obtained during a multi-objective topology optimization based on the finite-element method and genetic algorithm (GA) is used for training of the convolutional neural network (CNN). Two different approaches are proposed: 1) CNN trained by preliminary optimization with a small population for GA is used for the main optimization with a large population and 2) CNN is used for screening of torque performances in the optimization with respect to the motor efficiency.

Source: IEEE Transactions on Magnetics

DOI: 10.1109/tmag.2019.2899934

Authors: Shuhei Doi, Hidenori Sasaki, Hajime Igarashi

Institution: Hokkaido University

Language: English

Topic: Topology Optimization in Engineering

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: Japan Society for the Promotion of Science