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Abstract Topology optimization has emerged as a versatile design tool embraced across diverse domains. This popularity has led to great efforts in the development of education-centric topology optimization codes with various focuses, such as targeting beginners seeking user-friendliness and catering to experienced users emphasizing computational efficiency. In this study, we introduce , a novel 2D and 3D topology optimization software developed in Python and built upon the open-source library, designed to harmonize usability with computational efficiency and post-processing for fabrication. employs a modular architecture, offering a unified input script for defining topology optimization problems and six replaceable modules to streamline subsequent optimization tasks. By enabling users to express problems in the weak form, eliminates the need for matrix manipulations, thereby simplifying the modeling process. The software also integrates automatic differentiation to mitigate the intricacies associated with chain rules in finite element analysis and sensitivity analysis. Furthermore, provides access to a comprehensive array of readily available solvers and preconditioners, bolstering flexibility in problem-solving. is designed for scalability, furnishing robust support for parallel computing that seamlessly adapts to diverse computing platforms, spanning from laptops to distributed computing clusters. It also facilitates effortless transitions for various spatial dimensions, mesh geometries, element types and orders, and quadrature degrees. Apart from the computational benefits, facilitates the automated exportation of optimized designs, compatible with open-source software for post-processing. This functionality allows for visualizing optimized designs across diverse mesh geometries and element shapes, automatically smoothing 3D designs, and converting smoothed designs into STereoLithography (STL) files for 3D printing. To illustrate the capabilities of , we present five representative examples showcasing topology optimization across 2D and 3D geometries, structured and unstructured meshes, solver switching, and complex boundary conditions. We also assess the parallel computational efficiency of by examining its performance across diverse computing platforms, process counts, problem sizes, and solver configurations. Finally, we demonstrate a physical 3D-printed model utilizing the STL file derived from the design optimized by . These examples showcase not only ’s rich functionality but also its parallel computing performance. The open-source is given in Appendix B and will be available to download athttps://github.com/missionlab/fenitop. 

Anisotropy in additive manufacturing (AM), particularly in the material extrusion process, plays a crucial role in determining the actual structural performance, including the stiffness and strength of the printed parts. Unless accounted for, anisotropy can compromise the objective performance of topology-optimized structures and allow premature failures for stress-sensitive design domains. This study harnesses process-induced anisotropy in material extrusion-based 3D printing to design and fabricate stiff, strong, and lightweight structures using a two-step framework. First, an AM-oriented anisotropic strength-based topology optimization formulation optimizes the structural geometry and infill orientations, while assuming both anisotropic (i.e., transversely isotropic) and isotropic infill types as candidate material phases. The dissimilar stiffness and strength interpolation schemes in the formulation allow for the optimized allocation of anisotropic and isotropic material phases in the design domain while satisfying their respective Tsai–Wu and von Mises stress constraints. Second, a suitable fabrication methodology realizes anisotropic and isotropic material phases with appropriate infill density, controlled print path (i.e., infill directions), and strong interfaces of dissimilar material phases. Experimental investigations show up to 37% improved stiffness and 100% improved strength per mass for the optimized and fabricated structures. The anisotropic strength-based optimization improves load-carrying capacity by simultaneous infill alignment along the stress paths and topological adaptation in response to high stress concentration. The adopted interface fabrication methodology strengthens comparatively weaker anisotropic joints with minimal additional material usage and multi-axial infill patterns. Furthermore, numerically predicted failure locations agree with experimental observations. The demonstrated framework is general and can potentially be adopted for other additive manufacturing processes that exhibit anisotropy, such as fiber composites. 

It is now a well-established fact that even simple topology variations can drastically change the fracture response of structures. With the objective of gaining quantitative insight into this phenomenon, this paper puts forth a density-based topology optimization framework for the fracture response of structures subjected to quasistatic mechanical loads. One of the two key features of the proposed framework is that it makes use of a complete phase-field fracture theory that has been recently shown capable of accurately describing the nucleation and propagation of brittle fracture in a wide range of nominally elastic materials under a wide range of loading conditions. The other key feature is that the framework is based on a multi-objective function that allows optimizing in a weighted manner: ( ) the initial stiffness of the structure, ( ) the first instance at which fracture nucleates, and ( ) the energy dissipated by fracture propagation once fracture nucleation has occurred. The focus is on the basic case of structures made of a single homogeneous material featuring an isotropic linear elastic behavior alongside an isotropic strength surface and toughness. Novel interpolation rules are proposed for each of these three types of material properties. As a first effort to gain quantitative insight, the framework is deployed to optimize the fracture response of 2D structures wherein the fracture is bound to nucleate in three different types of regions: within the bulk, from geometric singularities (pre-existing cracks and sharp corners), and from smooth parts of the boundary. The obtained optimized structures are shown to exhibit significantly enhanced fracture behaviors compared to those of structures that are optimized according to conventional stiffness maximization. Furthermore, the results serve to reveal a variety of strengthening and toughening mechanisms. These include the promotion of highly porous structures, the formation of tension-compression asymmetric regions, and the removal of cracks and sharp corners. The particular mechanism that is preferred by a given structure, not surprisingly, correlates directly to the elastic, strength, and toughness properties of the material that is made of.