A novel feature mapping topology optimization method is presented, allowing for the creation of features with highly flexible shapes. The method easily integrates with conventional density-based formulations. Feature shapes are implicitly described by NURBS control points. The feature shape dictates the locations of two sets of projection points to represent the solid void boundaries. At these projection points, density values are projected onto a finite element mesh. The method optimizes feature shapes in a gradient-based manner, while allowing more specific control of the feature shapes than classical level set methods. Several feature fields can be combined to create a final output design. It is found that the eminent flexibility of the NURBS-based feature definition is a benefit but also requires additional regularization to guarantee stability of the optimization.

A framework is proposed for geometric filters in density-based topology optimization. Most geometric filters feature density detection in a specified region of interest. In this method, this operation is decoupled from the finite element mesh by using interpolated densities. This allows for the filter configuration (e.g. tool orientation or printing direction) to be optimized simultaneously with the geometric layout. The framework is presented in a generic manner, and demonstrated on filters for: milling with simultaneous optimization of milling orientation; jetting with simultaneous optimization of jetting positions; and printing with simultaneous optimization of printing orientation. The performance of the framework is tested with numerical examples for compliance in 2D and 3D on a structured mesh, and in 2D on an unstructured mesh. The framework can extend the design freedom of existing filters, and can serve as a basis for the development of new geometric filters.

Topology optimization methods are used to design high performance structural components that often have complex geometric layouts. In several industries, components are required to be cleanable, and for this research cleaning by jetting is considered. Thus, being able to ensure jet access on the entire surface of a structure is of interest in topology optimization. In this paper, a jetting filter is proposed, that turns a blueprint design into a jet accessible design. Two methods are considered to find an access field for each jet. These individual jet access fields are then combined into a total access field, to obtain a cleanable design. Consistent sensitivity analysis is used and the additional computational cost of the jetting filter is modest compared to the finite element analysis. The performance of the two methods is demonstrated with 2D and 3D numerical examples for mechanical and thermal topology optimization problems.

This paper presents a density based topology optimization method for infinite fatigue life constraints of non-proportional load cases, with a specific focus on parts with cyclic symmetry. Considering non-proportional loads in topology optimization significantly broadens the types of design problems that can be handled. The method estimates the local variation in Signed von Mises stress using a smooth min/max function and constrains the resulting stress amplitude using established stress based topology optimization methods. Accounting for non-proportionality of loading significantly increases the computation cost with respect to existing proportional methods, as the time-varying stress field needs to be computed. Inertia effects are neglected in the structural analysis. Therefore, a quasi-static analysis is used to obtain the stress history. To reduce the computational cost, advantage is taken of cyclic symmetric properties to reduce the number of necessary time steps to evaluate. This reduces the computational cost roughly proportional to the number of unique load time steps present in the repeated segments as opposed to a standard implementation. The method is tested on numerical examples in 2D and 3D for both proportional and non-proportional loads and was found to be locally accurate up to the accuracy of the constraint aggregation.

Topology optimization methods improve the structural performance of components. However, in food processing, medical, high-precision, and other industries' designs should also fulfil the requirement of being cleanable. An important aspect of cleanability is drainability, which entails that fluids can always run off the structure under just gravity. Therefore, taking drainability into account during the optimization process is essential for many applications. This paper proposes a drainage filter that turns a blueprint design into a drainable design. In a layer-by-layer fashion, the design is adjusted, to ensure fluids can always run down its surface. A smooth minimum and maximum are used in the formulation to allow for consistent sensitivity calculation. To allow for the small runoff angles, typical for practical drainability, a grid refinement is proposed. Moreover, any drainage direction can be considered. The effectiveness of the method is illustrated in 2D and 3D.

This work considers an efficient approach for length scale control in high-resolution three-dimensional level set–based topology optimization. A contrast parameter–based cut element method is employed to ensure a crisp interface representation while working on fixed background hexahedral meshes. This enables the use of efficient multigrid preconditioned Krylov methods in massively parallelized computations. The minimum length scale is controlled using a projection filter without the need for β-continuation. The capabilities of the proposed approach is demonstrated on several numerical examples using more than 62 million hexahedral elements.

Variable thickness sheet and homogenization-based topology optimization often result in spread-out, non-well-defined solutions that are difficult to interpret or de-homogenize to sensible final designs. By extensive numerical investigations, we demonstrate that such solutions are due to non-uniqueness of solutions or at least very flat minima. Much clearer and better-defined solutions may be obtained by adding a measure of non-void space to the objective function with little if any increase in structural compliance. We discuss various alternatives for cleaning up solutions and propose two efficient approaches which both introduce an auxiliary field to control non-void space: one approach based on a cut element based auxiliary field (hybrid approach) and another approach based on an auxiliary element based field (density approach). At the end, we demonstrate significant qualitative and quantitative improvements in variable thickness sheet and de-homogenization designs resulting from the proposed cleaning schemes.