Human-Informed Topology Optimization: interactive application of feature size controls

Abstract

This paper presents a new topology optimization framework in which the design decisions are made by humans and machines in collaboration. The new Human-Informed Topology Optimization approach eases the accessibility of topology optimization tools and enables improved design identification for the so-called ‘everyday’ and ‘in-the-field’ design situations. The new framework is based on standard density-based compliance minimization. However, the design engineer is enabled to actively use their experience and expertise to locally alter the minimum feature size requirements. This is done by conducting a short initial solution and prompting the design engineer to evaluate the quality. The user can identify potential areas of concern based on the initial material distribution. In these areas, the minimum feature size requirement can be altered as deemed necessary by the user. The algorithm rigorously resolves the compliance problem using the updated filtering map, resulting in solutions that eliminate, merge, or thicken topological members of concern. The new framework is demonstrated on 2D benchmark examples and the extension to 3D is shown. Its ability to achieve performance improvement with few computational resources are demonstrated on buckling and stress concentration examples.