Lightweight construction is playing an increasingly important role for a wide variety of reasons, such as improving energy efficiency. In addition to lightweight material construction, lightweight structure construction is gaining more and more influence, which is made possible due to topology optimization. The aim of topology optimization is to develop an optimal design proposal based on a construction space model and given boundary conditions (e.g. mechanical or thermal). The calculation of the structural response is often done using the time consuming finite element method (FEM). Since topology optimization is an iterative process, usually many finite element analyses (FEA) have to be performed, which results in high computing time. Therefore, this article presents different methods to minimize computing time by exploiting various special features that occur with FEA in the context of optimizations.
In this paper, we develop a smoothing algorithm that allows a subsequent production of components directly after topology optimisation. This is achieved by keeping features that are important for production, such as flat surfaces or straight edges.The algorithm works in two steps. The first step is based on the marching cubes algorithm and is necessary to prepare the optimisation result for the second step. The optimisation result consists of a density distribution and needs to be transformed to a surface representation without further material or density information. The second step makes use of an implicit method for smoothing surfaces, the socalled implicit fairing.The proposed two-step algorithm is exemplarily shown on two models. The results are compared to those received from a commercial solution to evaluate the quality of the algorithm. We show that the proposed algorithm allows a subsequent production directly after the optimisation and leads to results that are similarly good compared to those obtained by the commercial solution.
We present a multi-objective topology optimization method based on the Non-Sorting Genetic Algorithm II (NSGA-II). The presented approach is a tool for early-stage engineering applications capable of providing insights into the complex relationship between structural features and the performance of a design without a priori assumptions about objective space. Mass reduction, linear elastic deformation, and stationary thermal conduction are considered simultaneously with three additional constraints. The specifically developed genotype-phenotype mapping ensures the practical benefit of obtained design propositions and significantly reduces computational effort to generate a dense set of Pareto solutions. The mapping procedure smooths probabilistically generated structures, removes unconnected material, and refines the spatial discretization for the subsequently used finite element solver. We present sets of Pareto optimal solutions to large three-dimensional design problems with multiple objectives and multiple near-application constraints that are feasible design propositions for engineering design. Geometrical features present in the obtained Pareto set are discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.