Abstract

This research paper explores the use of 3D-printing technologies in prototyping of Topology Optimization (TO) driven design. The paper describes the integration of TO into an early design work-flow and highlights the difficulties thereof. Due to the high computation times of TO, we outline the use of statistical learning to approximate TO material density results. Specifically, we highlight the use of such techniques for TO of thin-shell, non-volumetric geometries and the incorporation of specific assumptions related to such geometries to improve the functional approximation using statistical methods. We describe the various stages of the design pipeline that benefit from interactive TO.

Citation

Bhooshan, V., Fuchs, M. and Bhooshan, S., 2017, May. 3D-printing, topology optimization and statistical learning: a case study. In Proceedings of the Symposium on Simulation for Architecture and Urban Design (pp. 107-114).