An AI-Assisted Design Method for Topology Optimization without Pre-Optimized Training Data

DS 116: Proceedings of the DESIGN2022 17th International Design Conference

Year: 2022
Editor: Mario Štorga, Stanko Škec, Tomislav Martinec, Dorian Marjanović
Author: Alex Halle, Lucio Flavio Campanile, Alexander Hasse
Series: DESIGN
Institution: Chemnitz University of Technology, Germany
Section: Artificial Intelligence and Data-Driven Design
Page(s): 1589-1598
DOI number: https://doi.org/10.1017/pds.2022.161
ISSN: 2732-527X (Online)

Abstract

Engineers widely use topology optimization during the initial process of product development to obtain a first possible geometry design. The state-of-the-art method is iterative calculation, which requires both time and computational power. This paper proposes an AI-assisted design method for topology optimization, which does not require any optimized data. The presented AI-assisted design procedure generates geometries that are similar to those of conventional topology optimizers, but require only a fraction of the computational effort.

Keywords: topology optimisation, AI-assisted design, computational design methods, design evaluation, design to x (DtX)