A new non-gradient-based topology optimization algorithm with black–white density and manufacturability constraints

Abstract

Topology optimization (TO) is one of the growing research fields, and nongradient-based TO (NGTO) is a promising method. This is due to the algorithm’s ability to avoid local minima and solve TO with no gradient information. Furthermore, the NGTO deals better with manufacturability constraints. In this work, we propose a new NGTO algorithm using the simulated annealing (SA) algorithm, with connectivity criteria and black–white discrete density. The connectivity criterion analyzes the neighborhood of an element and checks if the neighboring elements have the same density, penalizing checkerboard solutions. The results showed that the algorithm converges to an optimized structure with compliance values similar or even enhanced compared to those found in the literature. Taking into account the advantages of being non-gradient and the ease of application, the proposed algorithm presents itself as a promising algorithm in the field of TO research. Since checkerboards are the main limit in the manufacturing of designed parts with topology optimization, the manufacturability constraints applied in the proposed algorithm. The binary non-gradient topology optimization with checkerboard-free structure is the main contribution of the proposed method.