In Metal Additive Manufacturing (MAM), support structures serve not only for mechanical supports but also for heat dissipation, preventing overheating in the melt zone. Although a high support volume aids heat dissipation, it significantly increases printing time, material wastage, and post-processing efforts. Additionally, contact area between the part and the supports often has higher surface roughness, which compromises part quality. This paper presents a novel density-based Topology Optimization (TO) technique for designing support structures optimized for efficient heat evacuation while keeping the part design fixed. First, a simplified MAM model, already established in the literature, is used to identify regions prone to overheating, referred to as ‘hotspots.’ This hotspot information is then used to formulate a TO problem that minimizes support volume while regulating the heat evacuation efficiency of the supports through thermal compliance which is defined as a constraint. For calculation of thermal compliance, a thermal load is defined using the hotspot information while the baseplate acts as a heat sink. To reduce post-processing costs, a concept of vicinity penalization is introduced, promoting the minimization of the part-support interface area. First, a set of 2D results is presented to demonstrate the method’s effectiveness and explain the influence of various parameters. Next, the TO algorithm is applied to a real-size 3D part and the results are discussed. Finally, the performance of the optimized supports is evaluated using a transient layer-by-layer AM simulation.

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.

The quality factor (Q factor) of nanomechanical resonators is influenced by geometry and stress, a phenomenon called dissipation dilution. Studies have explored maximizing this effect, leading to softly-clamped resonator designs. This paper proposes a topology optimization methodology to design two-dimensional nanomechanical resonators with high Q factors by maximizing dissipation dilution. A formulation based on the ratio of geometrically nonlinear to linear modal stiffnesses of a prestressed finite element model is used, with its corresponding adjoint sensitivity analysis formulation. Systematic design in square domains yields geometries with comparable Q factors to literature. We analyze the trade-offs between resonance frequency and quality factor, and how these are reflected in the geometry of resonators. We further apply the methodology to optimize a resonator on a full hexagonal domain. By using the entire mesh—i.e., without assuming any symmetries—we find that the optimizer converges to a two-axis symmetric design comprised of four tethers.

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.

The design of smart structures is challenging because of the integrated electromechanical modelling and optimization of actuators, sensors and load-bearing structures. To simplify the design process, it is common to decouple some of the components and physics and develop each part separately, which could lead to suboptimal systems. To improve the overall design of active structures, we propose an integrated and fully coupled design methodology for a certain class of smart structures. Specifically, this paper presents a numerical framework for the simultaneous application of density-based topology optimization of multi-material conductive compliant mechanisms and a composite multi-layered geometry-projection method for the optimization of the size, position and orientation of embedded piezoelectric stack actuators. Their electromechanical properties are represented in a continuum-based setting by an orientation- and geometry-dependent equivalent material model and their activation depends on the distribution of conductive material in the structure. Furthermore, a novel constraint on the polarization of the actuators is proposed to avoid unwanted designs that could cause their mechanical degradation. A set of numerical examples is analysed and discussed. The proposed framework exhibits promising results, with significant improvements in comparison to a benchmark problem.

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.

When employing the strut-and-tie modelling (STM) method in the conceptual design of reinforced concrete structures, a suitable strut-and-tie (ST) model indicating load transfer mechanisms first needs to be identified. Topology optimization (TO) methods have frequently been used for this purpose. However, although TO methods employing either a ground-structure or a continuum-based TO approach can be used, the performance and effectiveness of these two methods have not been systematically investigated and compared. To obtain a better understanding of the characteristics of both methods, a systematic comparison procedure is proposed to investigate the generation process and the resulting ST designs. Three aspects, relating to structural performance, economic issues, and method applicability are considered in the comparison, with six metrics formulated to quantify these aspects. Based on investigation of designs for three reinforced concrete elements incorporating typical discontinuity regions (two 2D cases and a 3D case), the performance of the two methods is assessed. It is found that both methods result in safe and efficient ST designs, with comparable structural performance, while some differences in terms of computation time and usability are observed.

Soft grippers are ideal for grasping delicate, deformable objects with complex geometries. Universal soft grippers have proven effective for grasping common objects, however complex objects or environments require bespoke gripper designs. Multi-material printing presents a vast design-space which, when coupled with an expressive computational design algorithm, can produce numerous, novel, high-performance soft grippers. Finding high-performing designs in challenging design spaces requires tools that combine rapid iteration, simulation accuracy, and fine-grained optimization across a range of gripper designs to maximize performance, no current tools meet all these criteria. Herein, a diversity-based soft gripper design framework combining generative design and topology optimization (TO) are presented. Compositional pattern-producing networks (CPPNs) seed a diverse set of initial material distributions for the fine-grained TO. Focusing on vacuum-driven multi-material soft grippers, several grasping modes (e.g. pinching, scooping) emerging without explicit prompting are demonstrated. Extensive automated experimentation with printed multi-material grippers confirms optimized candidates exceed the grasp strength of comparable commercial designs. Grip strength, durability, and robustness is evaluated across 15,170 grasps. The combination of fine-grained generative design, diversity-based design processes, high-fidelity simulation, and automated experimental evaluation represents a new paradigm for bespoke soft gripper design which is generalizable across numerous design domains, tasks, and environments.

In topology optimization of transient problems, memory requirements and computational costs often become prohibitively large due to the backward-in-time adjoint equations. Common approaches such as the Checkpointing (CP) and Local-in-Time (LT) algorithms reduce memory requirements by dividing the temporal domain into intervals and by computing sensitivities on one interval at a time. The CP algorithm reduces memory by recomputing state solutions instead of storing them. This leads to a significant increase in computational cost. The LT algorithm introduces approximations in the adjoint solution to reduce memory requirements and leads to a minimal increase in computational effort. However, we show that convergence can be hampered using the LT algorithm due to errors in approximate adjoints. To reduce memory and/or computational time, we present two novel algorithms. The hybrid Checkpointing/Local-in-Time (CP/LT) algorithm improves the convergence behavior of the LT algorithm at the cost of an increased computational time but remains more efficient than the CP algorithm. The Parallel-Local-in-Time (PLT) algorithm reduces the computational time through a temporal parallelization in which state and adjoint equations are solved simultaneously on multiple intervals. State and adjoint fields converge concurrently with the design. The effectiveness of each approach is illustrated with two-dimensional density-based topology optimization problems involving transient thermal or flow physics. Compared to the other discussed algorithms, we found a significant decrease in computational time for the PLT algorithm. Moreover, we show that under certain conditions, due to the use of approximations in the LT and PLT algorithms, they exhibit a bias toward designs with short characteristic times. Finally, based on the required memory reduction, computational cost, and convergence behavior of optimization problems, guidelines are provided for selecting the appropriate algorithms.

Soft robotic grasping has rapidly spread through the academic robotics community in recent years and pushed into industrial applications. At the same time, multimaterial 3D printing has become widely available, enabling the monolithic manufacture of devices containing rigid and elastic sections. We propose a novel design technique that leverages both technologies and can automatically design bespoke soft robotic grippers for fruit-picking and similar applications. We demonstrate the novel topology optimisation formulation that generates multi-material soft grippers, can solve internal and external pressure boundaries, and investigate methods to produce air-tight designs. Compared to existing methods, it vastly expands the searchable design space while increasing simulation accuracy.

Overheating is a major issue especially in metal Additive Manufacturing (AM) processes, leading to poor surface quality, lack of dimensional precision, inferior performance and/or build failures. A 3D density-based topology optimization (TO) method is presented which addresses the issue of local overheating during metal AM. This is achieved by integrating a simplified AM thermal model and a thermal constraint within the optimization loop. The simplified model, recently presented in literature, offers significant computational gains while preserving the ability of overheating detection. The novel thermal constraint ensures that the overheating risk of optimized designs is reduced. This is fundamentally different from commonly used geometry-based TO methods which impose a geometric constraint on overhangs. Instead, the proposed approach takes the process physics into account. The proposed method is validated via an experimental comparative study. Optical tomography (OT) is used for in-situ monitoring of process conditions during fabrication and obtained data is used for evaluation of overheating tendencies. The novel TO method is compared with two other methods: standard TO and TO with geometric overhang control. The experimental data reveals that the novel physics-based TO design experienced less overheating during the build as compared to the two classical designs. A study further investigated the correlation between overheating observed by high OT values and the defect of porosity. It shows that overheated regions indeed show higher defect of porosity. This suggests that geometry-based guidelines, although enhance printability, may not be sufficient for eliminating overheating issues and related defects. Instead, the proposed physics-based method is able to deliver efficient designs with reduced risk of overheating.

The design of high-performance mechatronic systems is very challenging, as it requires delicate balancing of system dynamics, the controller, and their closed-loop interaction. Topology optimization provides an automated way to obtain systems with superior performance, although extension to simultaneous optimization of both topology and controller has been limited. To allow for topology optimization of mechatronic systems for closed-loop performance, stability, and disturbance rejection (i.e. modulus margin), we introduce local approximations of the Nyquist curve using circles. These circular approximations enable simple geometrical constraints on the shape of the Nyquist curve, which is used to characterize the closed-loop performance. Additionally, a computationally efficient robust formulation is proposed for topology optimization of dynamic systems. Based on approximation of eigenmodes for perturbed designs, their dynamics can be described with sufficient accuracy for optimization, while preventing the usual threefold increase of additional computational effort. The designs optimized using the integrated approach have significantly better performance (up to 350% in terms of bandwidth) than sequentially optimized systems, where eigenfrequencies are first maximized and then the controller is tuned. The proposed approach enables new directions of integrated (topology) optimization, with effective control over the Nyquist curve and efficient implementation of the robust formulation.

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.

In design optimization of bulk handling equipment (BHE) we generally focus on the mean performance of the equipment. However, granular materials behave stochastic due to irregularities in particle shape and size which leads to stochastic performance of the equipment. To include the stochastic performance we propose robust metamodel-based design optimization (MBDO). The used metamodels are trained with stochastic performance data from randomly repeated discrete element method (DEM) simulations and predict mean and variance of the equipment performance. This method is compared to the conventional deterministic optimization method by means of a case study of a discharging hopper including verification and validation. The robust MBDO shows more distinctive optimal designs compared to the deterministic approach. In addition, the DEM-based metamodel is a relatively accurate method to predict DEM-model simulation results. However, the validation indicates that differences between DEM-model and experimental results highly affect the reliability of the found optima.

Computational process modelling of metal additive manufacturing has gained significant research attention in recent past. The cornerstone of many process models is the transient thermal response during the AM process. Since deposition-scale modelling of the thermal conditions in AM is computationally expensive, spatial and temporal simplifications, such as simulating deposition of an entire layer or multiple layers, and extending the laser exposure times, are commonly employed in the literature. Although beneficial in reducing computational costs, the influence of these simplifications on the accuracy of temperature history is reported on a case-by-case basis. In this paper, the simplifications from the existing literature are first classified in a normalised simplification space based on assumptions made in spatial and temporal domains. Subsequently, all types of simplifications are investigated with numerical examples and compared with a high-fidelity reference model. The required numerical discretisation for each simplification is established, leading to a fair comparison of computational times. The holistic approach to the suitability of different modelling simplifications for capturing thermal history provides guidelines for the suitability of simplifications while setting up a thermal AM model.

Compliant mechanisms actuated by pneumatic loads are receiving increasing attention due to their direct applicability as soft robots that perform tasks using their flexible bodies. Using multiple materials to build them can further improve their performance and efficiency. Due to developments in additive manufacturing, the fabrication of multi-material soft robots is becoming a real possibility. To exploit this opportunity, there is a need for a dedicated design approach. This paper offers a systematic approach to developing such mechanisms using topology optimization. The extended SIMP scheme is employed for multi-material modeling. The design-dependent nature of the pressure load is modeled using the Darcy law with a volumetric drainage term. Flow coefficient of each element is interpolated using a smoothed Heaviside function. The obtained pressure field is converted to consistent nodal loads. The adjoint-variable approach is employed to determine the sensitivities. A robust formulation is employed, wherein a min-max optimization problem is formulated using the output displacements of the eroded and blueprint designs. Volume constraints are applied to the blueprint design, whereas the strain energy constraint is formulated with respect to the eroded design. The efficacy and success of the approach are demonstrated by designing pneumatically actuated multi-material gripper and contractor mechanisms. A numerical study confirms that multiple-material mechanisms perform relatively better than their single-material counterparts.

Concrete, as the most widely used construction material, is associated with a high environmental impact. Within the present study, structural optimization is the method of choice to counter this issue. The entire process, from optimization, to design, experiments and numerical simulation is outlined. Embedded in the framework of a design competition (Concrete Girder Optimization Competition 2021), a bridge between structural engineering and mathematical optimization is demonstrated. Two design concepts for optimized concrete girders, one with internal and one with external reinforcement, yet both based on strut-and-tie modeling, were investigated. Within the boundaries of the competition, several conclusions can be drawn: The results indicate the importance of an adequate structural interpretation of topology optimization results to obtain satisfying structural performance. The environmental evaluation outlines that the reinforcement mass has a substantial share in the total Global Warming Potential. A successful numerical re-simulation of selected girders can serve as a modeling base for other researchers. Compared to a conventionally designed girder an increase in resource efficiency, measured by load-carrying capacity versus environmental impact, of more than 30% was achieved.

Additive manufacturing (AM) processes have proven to be a perfect match for topology optimization (TO), as they are able to realize sophisticated geometries in a unique layer-by-layer manner. From a manufacturing viewpoint, however, there is a significant likelihood of process-related defects within complex geometrical features designed by TO. This is because TO seldomly accounts for process constraints and conditions and is typically perceived as a purely geometrical design tool. On the other hand, advanced AM process simulations have shown their potential as reliable tools capable of predicting various process-related conditions and defects. Thus far, geometry design by topology optimization and multiphysics manufacturing simulations have been viewed as two mostly separate paradigms, whereas one should really conceive them as one holistic computational design tool. More specifically, AM process models provide input to physics-based TO, where consequently, not only the designed component will function optimally, but also will have near-to-minimum manufacturing defects. In this regard, we aim at giving a thorough overview of holistic computational design tool concepts applied within AM. First, literature on TO for performance optimization is reviewed and then the most recent developments within physics-based TO techniques related to AM are covered. Process simulations play a pivotal role in the latter type of TO and serve as additional constraints on top of the primary end-user optimization objectives. As a natural consequence of this, a comprehensive and detailed review of non-metallic and metallic additive manufacturing simulations is performed, where the latter is divided into micro-scale and deposition-scale simulations. Material multi-scaling techniques, which are central to the process-structure-property relationships, are reviewed next, followed by a subsection on process multi-scaling techniques, which are reduced-order versions of advanced process models and are incorporable into physics-based TO due to their lower computational requirements. Finally the paper is concluded and suggestions for further research paths discussed.

Topology optimization has seen increased interest with the rise of additive manufacturing (AM) as a fabrication method, because of its ability to exploit the geometric complexity that AM offers. However, AM still imposes some geometric restrictions on the design, most notably on minimum feature size, overhang angles, and enclosed voids. Enclosed voids are problematic because for many AM methods it is impossible to remove supports, unmelted powder or uncured liquid from them. This paper introduces a filter based on a cumulative sum flood fill algorithm to alleviate this issue in a flexible manner. This filter produces a density field where every enclosed void element is rendered solid. It successfully eliminates enclosed voids in both 2D and 3D problems, with low computational cost due to its geometric nature. In addition we demonstrate direct control over the location, amount, and size of powder removal features by varying boundary conditions for the filter, running additional flood fills, and adding morphology operators, respectively.