Achieving binary topology optimization solutions via automatic projection parameter increase

TL;DR

This paper introduces an automatic method for increasing the threshold projection parameter in density-based topology optimization, enabling near-binary solutions without problem-specific tuning, demonstrated on various 2D benchmarks.

Contribution

A novel exponential growth-based approach for automatic threshold parameter increase that adapts dynamically during optimization, eliminating the need for hyper-parameter tuning across different problems.

Findings

Effective in achieving near-binary designs

Applicable to linear and nonlinear 2D problems

Reduces need for problem-specific parameter tuning

Abstract

A method is created to automatically increase the threshold projection parameter in three-field density-based topology optimization to achieve a near binary design. The parameter increase each iteration is based on an exponential growth function, where the growth rate is dynamically changed during optimization by linking it to the change in objective function. This results in a method that does not need to be tuned for specific problems, or optimizers, and the same set of hyper-parameters can be used for a wide range of problems. The effectiveness of the method is demonstrated on several 2D benchmark problems, including linear buckling and geometrically nonlinear problems.