Sierk Fiebig

An Element Deactivation and Reactivation Scheme for the Topology Optimization Based on the Density Method

Topology optimization results based on the homogenization or density method highly depend on the discretization of the design space. The smallest possible dimension of the optimized structure is one element edge length. If the mesh can be refined, smaller design features can be represented and thereby the performance of the optimized structures can be improved. But the mesh refinement is limited by the computational cost of the Finite Element Analysis (FEA).

Topology Optimization with Integrated Casting Simulation and Parallel Manufacturing Process Improvement

Affordable lightweight design is one of the key topics for the automotive future. Increasing worldwide competition, stricter legal guidelines and weight-intensive electronic components of new drive systems require lighter and more economic chassis components. Aluminum parts enable activation of lightweight design potentials in terms of good material properties and affordable costs. To ensure cost-effective production of cast parts, a lean and efficient manufacturing process is necessary. Furthermore, a lighter design lowers the material costs of the part directly. Topology optimization offers a lightweight design in a fast automatic procedure and is nowadays used at the beginning of many development processes for cast parts. Commercial optimization software considers the castability of the part only insufficiently on the basis of simple geometric rules. Thus, manual modifications for the manufacturing process are needed.

Considering Linear Buckling for 3D Density Based Topology Optimization

Stability is an important issue in topology optimization, since results of the optimization are often framework structures. If some trusses of these structures are subjected to compression, they maybe buckle and the structure fails.

Adaptive Topology and Shape Optimization with Integrated Casting Simulation

The automotive future demands light and affordable designs and components. To develop lighter parts in a decreased time structural optimization is used in many R&D departments.

Enhanced Firefly Algorithm with Implicit Movement

The question for an optimal solution to a certain real-world problem often turns into a complex optimization problem. The sizing of the cross sections for bars of a truss structure is generally hampered by interdependencies. This prevents local search methods from finding a sufficient optimum. For those issues, there is a demand for fast and reliable global optimization algorithms. The Firefly Algorithm is a swarm-intelligence-based method frequently used for solving multi-modal optimization problems. The algorithm maintains a set of individuals, each corresponding to a point within the solution space. During the optimization process, the individuals move within the solution space under certain rules in order to find the global optimum. This paper presents an enhancement of the Firefly Algorithm by an implicit backward Euler movement. Therefore, in each iteration a linear system of equations must be solved to determine the new positions of the individuals. To evaluate the performance of the implicit movement, it is applied to continues benchmark optimization functions. The optimization process is compared to the basic Firefly Algorithm to specify the effect of implicit movement. Furthermore, a discrete parameter optimization of a ten-bar truss in the sense of a weight reduction is carried out. The optimization results are compared to the basic Firefly Algorithm as well as to the results of four state-of-the-art algorithms. The implicit movement provides an intuitive and an easy to implement modification of the Firefly Algorithm. Simulation results show that the implicit movement causes a significant improvement in the convergence behavior compared to the basic Firefly Algorithm and outperforms state-of-the-art algorithms in terms of the solution quality and convergence behavior. Due to its generality, the proposed implicit movement can be implemented to several swarm-intelligence-based algorithms and offers a promising universal approach for enhancement.

Optimization of Manufacturing Tolerances on Sheet Metal Components in the Development Process

In an increasingly challenging environment of growing competition and stricter legal regulations, the concept of lightweight design gets more and more important for the automotive industry. Usually, the goals of lightweight tend to conflict with the robustness of the designs. The scattering of the geometry within the manufacturing tolerance leads to a divergence between the test bench results and the prediction from simulation. For an optimized lightweight design, this deviation will often lead to a violation of the requirements. In this case, the part has to be re-designed to improve its robustness, causing an additional time expense, higher costs and a higher weight.

Fertigungsgerechte Bauteilgestaltung in der Topologieoptimierung auf Grundlage einer integrierten Gießsimulation

In dem heutigen Entwicklungsprozess von Strukturbauteilen hat die Topologieoptimierung einen festen Platz. Als Grundlage fur heutige Topologieoptimierungssoftware konnen die mathematischen Verfahren Homogenisierungsmethode und die „Solid Isotropic Material with Penalization“ (SIMP) Methode von Bensoe et al. gelten [1, 2]. Andere, spater entwickelte Methoden verwenden heuristische Ansatze, die auf biologischem Wachstum basieren, [3] oder binden Level-Set-Funktionen ein [4].

Improving the development of sheet structures with optimization and simulation methods

Nowadays the development of mechanical components is driven by ambitious targets. So engineers have to fulfill all technical requirements and have to reduce weight and cost of the mechanical components simultaneously. Beside this, the development time and costs have to be lowered in order to reach shorter product cycles and faster market innovations.