Residual stresses and distortions are major barriers to the broader adoption of wire arc additive manufacturing. These issues are coupled and arise due to large thermal gradients and phase transformations during the directed energy deposition process. Mitigating distortions may lead to substantial residual stresses, causing cracks in the fabricated components. In this paper, we propose a novel method to reduce both residual stresses and distortions by optimizing the fabrication sequence. This approach explores the use of non-planar layers, leveraging the increased manufacturing flexibility provided by robotic arms. Additionally, our method allows for the concurrent optimization of the structural layout and corresponding fabrication sequence. We employ the inherent strain method as a simplified process simulation model to predict residual stresses and distortions. Local residual stresses are aggregated using a p-norm function, which is integrated into distortion minimization as a constraint. Through numerical examples, we demonstrate that the optimized non-planar fabrication strategies can effectively reduce both residual stresses and distortions.

Maintaining the maximum stiffness of components with as little material as possible is an overarching objective in computational design and engineering. It is well-established that in stiffness-optimal designs, material is aligned with orthogonal principal stress directions. In the limit of material volume, this alignment forms micro-structures resembling quads or hexahedra. Achieving a globally consistent layout of such orthogonal micro-structures presents a significant challenge, particularly in three-dimensional settings. In this paper, we propose a novel geometric algorithm for compiling stress-aligned hexahedral lattice structures. Our method involves deforming an input mesh under load to align the resulting stress field along an orthogonal basis. The deformed object is filled with a hexahedral grid, and the deformation is reverted to recover the original shape. The resulting stress-aligned mesh is used as basis for a final hollowing procedure, generating a volume-reduced stiff infill composed of hexahedral micro-structures. We perform quantitative comparisons with structural optimization and hexahedral meshing approaches and demonstrate the superior mechanical performance of our designs with finite element simulation experiments.

Additive manufacturing (AM) facilitates the production of complex structures. It is often combined with structural design by topology optimization to create lightweight structures with minimal material and maximum stiffness. In this paper, we consider the design of lightweight structures to be created by casting in formwork that is produced by additive manufacturing. This problem, arising from the building industry, relates to but differs from prior work on topology optimization for structures that are directly produced by additive manufacturing. Specifically, formwork is not permitted to contain extra supports in casting space since otherwise it results in casting blockages. Moreover, topological structures with cavities cannot be produced through casting. This work presents a topology optimization method for designing structures to be cast in AM-produced formwork. This approach addresses these two key challenges: (i) ensuring the formwork is self-supporting during printing to eliminate the need for additional supports, and (ii) designing the structure to be free of internal enclosed cavities, which would otherwise lead to disconnected or floating parts in the formwork. The effectiveness of the proposed method was demonstrated through several numerical examples and experimental evaluations. Results show that the formwork can be printed without extra supports, and internal enclosed cavities in optimized structures can be fully eliminated. The findings provide a new strategy to produce the lightweight structure and corresponding structural formwork.

Recent years have seen a growing interest in topology optimization of functionally graded microstructures, characterized by an array of microstructures with varying volume fractions. However, microstructures optimized at slightly different volume fractions do not necessarily connect well when placed adjacently. Furthermore, optimization is commonly performed on a finite set of volume fractions, limiting the number of microstructure configurations. In this paper, we introduce the concept of differentiable microstructures, which are parameterized microstructures that exhibit continuous variations in both geometry and mechanical properties. To construct such microstructures, we propose a novel formulation for topology optimization. In this approach, a series of 2-dimensional microstructures is represented using a height field, and the objective is to maximize the bulk modulus of the entire series. Through this optimization process, an initial microstructure with a small volume fraction undergoes non-uniform transformations, generating a series of microstructures with progressively increasing volume fractions. Notably, when compared to traditional uniform morphing methods, our proposed optimization approach yields a series of microstructures with bulk moduli that closely approach the theoretical limit.

Wire and Arc Additive Manufacturing (WAAM) has great potential for efficiently producing large metallic components. However, like other additive manufacturing techniques, materials processed by WAAM exhibit anisotropic properties. Assuming isotropic material properties in design optimization thus leads to less efficient material utilization. Instead of viewing WAAM-induced material anisotropy as a limitation, we consider it an opportunity to improve structural performance. This requires the integration of process planning into structural design. In this paper, we propose a novel method to utilize material anisotropy to enhance the performance of structures both during fabrication and in their use. Our approach is based on space–time topology optimization, which simultaneously optimizes the structural layout and the fabrication sequence. To model material anisotropy in space–time topology optimization, we derive the material deposition direction from the gradient of the pseudo-time field, which encodes the fabrication sequence. Numerical results demonstrate that leveraging material anisotropy effectively improves the performance of intermediate structures during fabrication as well as the overall structure.

In additive manufacturing, the fabrication sequence has a large influence on the quality of manufactured components. While planning of the fabrication sequence is typically performed after the component has been designed, recent developments have demonstrated the possibility and benefits of simultaneous optimization of both the structural layout and the corresponding fabrication sequence. This is particularly relevant in multi-axis additive manufacturing, where rotational motion offers enhanced flexibility compared to planar fabrication. The simultaneous optimization approach, called space–time topology optimization, introduces a pseudo-time field to encode the manufacturing process order, alongside a pseudo-density field representing the structural layout. To comply with manufacturing principles, the pseudo-time field needs to be monotonic, i.e., free of local minima. However, explicitly formulated constraints proposed in prior work are not always effective, particularly for complex structural layouts that commonly result from topology optimization. In this paper, we introduce a novel method to regularize the pseudo-time field in space–time topology optimization. We conceptualize the monotonic additive manufacturing process as a virtual heat conduction process starting from the surface upon which a component is constructed layer by layer. The virtual temperature field, which shall not be confused with the actual temperature field during manufacturing, serves as an analogy for encoding the fabrication sequence. In this new formulation, we use local virtual heat conductivity coefficients as optimization variables to steer the temperature field and, consequently, the fabrication sequence. The virtual temperature field is inherently free of local minima due to the physics it resembles. We numerically validate the effectiveness of this regularization in space–time topology optimization under process-dependent loads, including gravity and thermomechanical loads.

Mechanical metamaterials signify a groundbreaking leap in material science and engineering. The intricate and experience-dependent design process poses a challenge in uncovering architectural material sequences with exceptional mechanical properties. This study introduces inverse-designed 3D sequential metamaterials with outstanding mechanical attributes, achieved through a novel computational framework. The explored sequences based on Schoen's I-graph–wrapped package (IWP) and Schwarz Primitive (Schwarz P) surpass the Hashin-Shtrikman upper bound of Young's modulus at relative densities of 0.24 and 0.43, outperforming previous records. Optimized Body-Centered-Cubic (BCC) truss-based sets outperform traditional ones by 72.7%. This innovative approach can be extended for metamaterial customization, involving the optimization of multi-directional Young's modulus, total stiffness, and the addition of isotropy constraints. The paper explores the characteristics and implications of this innovation, emphasizing the impact of geometric and topological variations on mechanical performance. These metamaterial sequences offer unparalleled adaptability, and hold significant potential in structural engineering and adaptive mechanical systems, opening avenues for technological advancements.

Effective treatment of large acetabular defects remains among the most challenging aspects of revision total hip arthroplasty (THA), due to the deficiency of healthy bone stock and degradation of the support columns. Generic uncemented components, which are favored in primary THA, are often unsuitable in revision cases, where the bone-implant contact may be insufficient for fixation, without significant reaming of the limited residual bone. This study presents a computational design strategy for automatically generating patient-specific implants that simultaneously maximize the bone-implant contact area, and minimize bone reaming while ensuring insertability. These components can be manufactured using the same additive manufacturing methods as porous components and may reduce cost and operating-time, compared to existing patient-specific systems. This study compares the performance of implants generated via the proposed method to optimally fitted hemispherical implants, in terms of the achievable bone-implant contact surface, and the volume of reamed bone. Computer-simulated results based on the reconstruction of a set of 15 severe pelvic defects (Paprosky 2A-3B) suggest that the patient-specific components increase bone-implant contact by 63% (median: 63%; SD: 44%; 95% CI: 52.3%–74.0%; RMSD: 42%), and reduce the volume of reamed bone stock by 97% (median: 98%; SD: 4%; 95% CI: 95.9%–97.4%; RMSD: 3.7%).

Aligning lattice infills with the principal stress directions in loaded objects is crucial for improving stiffness. However, this principle only works for a single loading condition, where the stress field in 2D is described by two orthogonal principal stress directions. In this paper, we introduce a novel approach for designing and optimizing triangular lattice structures to accommodate multiple loading conditions, i.e., multiple stress fields need to be considered. Our method comprises two main steps: homogenization-based topology optimization and geometry-based de-homogenization. To ensure geometric regularity of the triangular lattices, we propose a simplified version of the general rank-3 laminate and parameterize the design domain using equilateral triangles with unique edge thickness. During optimization, edge thicknesses and orientations are adjusted based on the homogenized properties of the lattice. Our numerical findings demonstrate that this simplification introduces only a slight decrease in stiffness of less than 5% compared to using the general rank-3 laminate, and results in lattice structures with compelling geometric regularity. For geometry-based de-homogenization, we adopt a field-aligned triangulation approach to generate a globally consistent triangle mesh in which each triangle is oriented according to the optimized orientation field. Our approach for handling multiple loading conditions, akin to de-homogenization techniques for single loading conditions, yields highly detailed, optimized and spatially varying lattice structures. The method is computationally efficient, as simulations and optimizations are conducted at a low-resolution discretization of the design domain. Furthermore, since our approach is geometry-based, obtained structures are encoded into a compact geometric format that facilitates downstream operations such as editing and fabrication.

Additive manufacturing of metal parts involves phase transformations and high temperature gradients which lead to uneven thermal expansion and contraction, and, consequently, distortion of the fabricated components. The distortion has a great influence on the structural performance and dimensional accuracy, e.g., for assembly. It is therefore of critical importance to model, predict and, ultimately, reduce distortion. In this paper, we present a computational framework for fabrication sequence optimization to minimize distortion in multi-axis additive manufacturing (e.g., robotic wire arc additive manufacturing), in which the fabrication sequence is not limited to planar layers only. We encode the fabrication sequence by a continuous pseudo-time field, and optimize it using gradient-based numerical optimization. To demonstrate this framework, we adopt a computationally tractable yet reasonably accurate model to mimic the material shrinkage in metal additive manufacturing and thus to predict the distortion of the fabricated components. Numerical studies show that optimized curved layers can reduce distortion by orders of magnitude as compared to their planar counterparts.

We present a novel dehomogenization approach for the efficient design of high-resolution load-bearing structures. The proposed approach builds upon a streamline-based parametrization of the design domain, using a set of space-filling and evenly spaced streamlines in the two mutually orthogonal direction fields that are obtained from homogenization-based topology optimization. Streamlines in these fields are converted into a graph, which is then used to construct a quad-dominant mesh whose edges follow the direction fields. In addition, the edge width is adjusted according to the density and anisotropy of the optimized orthotropic cells. In a number of numerical examples, we demonstrate the mechanical performance and regular appearance of the resulting structural designs and compare them with those from classic and contemporary approaches.

Patient-specific implants offer a host of benefits over their generic counterparts. Nonetheless, the design and optimization of these components present several technical challenges, among them being the need to ensure their insertability into the host bone tissue. This presents a significant challenge due to the tight-fitting nature of the bone-implant interface. This paper presents a novel insertability metric designed to efficiently assess whether a rigid body can be inserted into a tight-fitting cavity, without interference. In contrast to existing solutions, the metric is fully differentiable and can be incorporated as a design constraint into shape optimization routines. By exploiting the tight-fitting condition, the problem of planning an interference-free insertion path is reformulated as the search for a single interference-free movement, starting from the inserted configuration. We prove that if there exists any outward movement for which no interference is indicated, then the body can be fully extracted from or, equivalently, inserted into the cavity. This formulation is extremely efficient and highly robust with respect to the complexity of the geometry. We demonstrate the effectiveness and efficiency of our method by applying it to the optimization of two-dimensional (2D) and three-dimensional (3D) designs for insertability, subject to various design requirements. We then incorporate the proposed metric into the optimization of an acetabular cup used in total hip replacement (THR) surgery where geometric and structural requirements are considered.

The summer school is realized as a joint teaching project between several partner universities under the auspices of the IDEA League alliance. The school trains students and young researchers on exploring and applying the potential of computational-based strategies in design for additive manufacturing, while engaging in dedicated team working activities. The aim of this work is to report on and discuss the success of the summer school in terms of the learning goals achievement. The work is based on the results of a one-day workshop with the summer school alumni showing how the acquired knowledge, skills and competences impacted their studies, research, and professional careers in a long-term after the course is finished, and outlining possible course content improvements to plan the summer school in 2023 that will take place with physical presence.

Material Extrusion (MEX) systems with dual-material capability can unlock interesting applications where flexible and rigid materials are combined. When chemically incompatible materials are concerned the adhesion between the two might be insufficient. Therefore researchers typically rely on dovetail type interlocking geometries in order to affix two bodies mechanically. However, dovetail type interlocking introduces extrusion discontinuities and relies on the material’s resistance to deformation, which is difficult to model. We propose a simple and effective 3D lattice consisting of interlaced horizontal beams in vertically alternating directions which interlock topologically: the interlaced topologically interlocking lattice (ITIL). It ensures continuous extrusion and ensures an interlock even for highly flexible materials. We develop analytical models for optimizing the ultimate tensile strength of the ITIL lattice in two different orientations relative to the interface: straight and diagonal. The analytical models are applied to polypropylene (PP) and polylactic acid (PLA) and verified by finite elements method (FEM) simulations and physical tensile experiments. In the diagonal orientation ITIL can obtain 82% of the theoretical upper bound of 8.6 MPa. ITIL seems to perform comparably to dovetail interlocking designs, while it lends itself to application to non-vertical interfaces. Optimizing the lattice for non-vertical interfaces, however, remains future work.

Density-based topology optimization using global and local volume constraints is a key technique to automatically design lightweight structures. It is known that stiffness optimal structures comprise spatially varying geometric patterns that span multiple length scales. However, both variants of topology optimization have challenges to efficiently converge to such a structural layout. In this paper, we investigate material layouts that are generated from stress trajectories, i.e., to compile a globally consistent structure by tracing the stress trajectories from finite element simulation of the solid design domain under external loads. This is particularly appealing from a computational perspective, since it avoids iterative optimization that involves finite element analysis on fine meshes. By regularizing the thickness of each trajectory using derived strain energy measures along them, stiff structural layouts can be generated in a highly efficient way. We then shed light on the use of the resulting structures as initial density fields in density-based topology optimization, i.e., to generate an initial density field that is then further optimized via topology optimization. We demonstrate that by using a stress trajectory guided density initialization in lieu of a uniform density field, convergence issues in density-based topology optimization can be significantly relaxed at comparable stiffness of the resulting structural layouts.

In this paper, we present novel algorithms for visualizing the three mutually orthogonal principal stress directions in 3D solids under load and we discuss the efficient integration of these algorithms into the 3D Trajectory-based Stress Visualizer (3D-TSV), a visual analysis tool for the exploration of the principal stress directions of 3D stress field. In the design of 3D-TSV, several perceptual problems have been solved. We present a novel algorithm for generating a space-filling and evenly spaced set of stress lines. The algorithm obtains a more
regular appearance by considering the locations of lines, and enables the extraction of a level-of-detail representation with adjustable sparseness of the trajectories along a certain stress direction. A new combined visualization of two principal directions via oriented ribbons enables to convey ambiguities in the orientation of the principal stress directions. Additional depth cues have been added to improve the perception of the spatial relationships between trajectories. 3D-TSV provides a modular and generic implementation of key algorithms required for a trajectory-based visual analysis of principal stress directions, including the automatic seeding of space-filling stress lines, their extraction using numerical schemes, their mapping to an effective renderable
representation, and rendering options to convey structures with special mechanical properties. 3D-TSV is accessible to end users via a C++- and OpenGL-based rendering frontend that is seamlessly connected to a
MatLab-based extraction backend. The code (BSD license) of 3D-TSV as well as scripts to make ANSYS and ABAQUS simulation results accessible to the 3D-TSV backend are publicly available.

Recent advances in 3D printable micro-architected materials offer unprecedented possibilities for the development of highly tailored orthopaedic implants. These devices, which are typically made from fully solid materials, significantly alter load transmission to the surrounding bone tissue, potentially leading to interface instability and bone resorption. In this work, we present computational methods to synthesize three dimensional (3D), patient-specific, implants with heterogeneous micro-architecture. Our method simultaneously minimizes the risks of load-induced interface fracture and peri-prosthetic bone remodelling, while taking into account functional and manufacturing constraints.
We first develop a novel parametric micro-architecture with desirable functional attributes and a wide range of effective mechanical properties, including both positive and negative Poisson’s ratios. We then present formulations which optimize the spatial configuration of micro-architecture parameters in order to simultaneously minimize the risk of load-induced interface fracture and post-operative bone remodelling. To that end, a novel bone remodelling objective is devised, taking into account both bone apposition and resorption, predicted via a model based on strain–energy density. The interface fracture objective is defined as the maximum value of the multi-axial Hoffman failure criterion along the interface.
The procedure is applied to the design of 3D titanium hip implants with prescribed conventional geometries and compared, in silico, to both a conventional solid implant and a homogeneous low-stiffness lattice design. The optimized implant results in a performance improvement of 64.0% in terms of bone remodelling, and 13.2% in terms of interface fracture risk, compared to a conventional solid implant design.

We present OpenFish: an open source soft robotic fish which is optimized for speed and efficiency. The soft robotic fish uses a combination of an active and passive tail segment to accurately mimic the thunniform swimming mode. Through the implementation of a novel propulsion system that is capable of achieving higher oscillation frequencies with a more sinusoidal waveform, the open source soft robotic fish achieves a top speed of 0.85 m/s . Hereby, it outperforms the previously reported fastest soft robotic fish by 27% . Besides the propulsion system, the optimization of the fish morphology played a crucial role in achieving this speed. In this work, a detailed description of the design, construction and customization of the soft robotic fish is presented. Hereby, we hope this open source design will accelerate future research and developments in soft robotic fish.

Personalized designs bring added value to the products and the users. Meanwhile, they also pose challenges to the product design process as each product differs. In this paper, with the focus on personalized fit, we present an overview as well as details of the personalized design process based on design practice. The general workflow of personalized product design is introduced first. Then different steps in the workflow such as human data/parameters acquisition, computational design, design for digital fabrication, and product evaluation are presented. Tools and methods that are often used in different steps in the process are also outlined where in human data acquisition, 3D scanning, and digital human models are addressed. For computational design, the use of computational thinking tools such as abstraction, decomposition, pattern recognition and algorithms are discussed. In design for digital fabrication, additive manufacturing methods (e.g. FDM), and their requirements on the design are highlighted. For product evaluation, both functional evaluation and usability evaluation are considered and the evaluation results can be the starting point of the next design iteration. Finally, several case studies are presented for a better understanding of the workflow, the importance of different steps in the workflow and the deviations in the approach regarding different contexts. In conclusion, we intend to provide designers a holistic view of the design process in designing personalized products as well as help practitioners trigger innovations regarding each step of the process.

The optimization of porous infill structures via local volume constraints has become a popular approach in topology optimization. In some design settings, however, the iterative optimization process converges only slowly, or not at all even after several hundreds or thousands of iterations. This leads to regions in which a distinct binary design is difficult to achieve. Interpreting intermediate density values by applying a threshold results in large solid or void regions, leading to sub-optimal structures. We find that this convergence issue relates to the topology of the stress tensor field that is simulated when applying the same external forces on the solid design domain. In particular, low convergence is observed in regions around so-called trisector degenerate points. Based on this observation, we propose an automatic initialization process that prescribes the topological skeleton of the stress field into the density field as solid simulation elements. These elements guide the material deposition around the degenerate points, but can also be remodelled or removed during the optimization. We demonstrate significantly improved convergence rates in a number of use cases with complex stress topologies. The improved convergence is demonstrated for infill optimization under homogeneous as well as spatially varying local volume constraints.