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Procedural and stochastic microstructures for functional fabrication – MuFFin

Procedural and stochastic microstructures for functional fabrication

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Challenges and objectives

Additive manufacturing of microstructures will play a decisive role in several fields, but several challenges remain. The size of printed objects is increasing and, in parallel, the available printing resolution is becoming finer. Current methods consider periodic structures, which allows for compact storage, efficient display, and simulation of macroscopic elastic behavior, but they exhibit poor gradation behavior. The MuFFin project sought to address these challenges by considering procedural, stochastic, and manufacturable foams with controlled macroscopic elastic behavior. Thanks to its procedural formulation, the introduced algorithms have constant complexity and can be scaled up. Due to their stochastic geometry, foams can have spatial variations in statistics and can be integrated into objects through stochastic modeling, which has overcome the limitations imposed by periodic structures. The lack of regularity allows a simple approach to vary the geometry, and therefore the mechanical properties of an object and its surface. Finally, the MuFFin project aimed to discover microstructures that can be manufactured with a wide range of technologies (SLS, FDM, SLA, etc.) without requiring support structures.

Inspired by procedural solids in computer graphics, MuFFin proposed to go beyond periodic tilings by studying procedural, stochastic, and manufacturable foams with a controlled macroscopic elastic behavior. A procedural geometry is defined as an implicit function evaluated at every point in space at the desired resolution. It does not require explicitly storing the geometry in the memory. Stochastic geometry enables an easier grading of material properties and conforming to a surface. Compared to periodic structures, the lack of global organization and periodicity allows for better gradation of stochastic geometry. The absence of regularity affords a simple approach to grade their geometry -- and thus their mechanical properties -- within a target object and its surface. Moreover, MuFFin aimed at controlling the macroscopic elastic behavior by connecting the parameters driving the procedural foam to the elastic properties. Finally, MuFFin aimed to discover microstructures that can be manufactured with a broad spectrum of technologies (SLS, FDM, SLA, etc.) without requiring supporting structures.

To the best of their knowledge, the project team has introduced the first class of foams that can be manufactured by filament deposition modeling. To achieve this, the Euclidean distance was replaced by a class of polyhedral distances to ensure that the 3D Voronoi diagram faces of any set of points define a geometrically self-supporting structure. Its usefulness has also been demonstrated to radically modify the kinematics of a soft robot, with the introduction of 2D/3D materials based on a growth process driven by a new star distance class. Remarkably, the geometry gradation is implicitly controlled by distance, unlike most existing methods that treat gradation explicitly. Finally, procedural noise was introduced for high-contrast anisotropic patterns, well-suited to the production of mechanical foams. This technique has been integrated by rendering software (e.g., Pixar's RenderMan) and used in some renderings of Star Wars Episode IX.

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Efremov, S.; Martínez, J.; Lefebvre, S. 3D Periodic Cellular Materials with Tailored Symmetry and Implicit Grading. Computer-Aided Design (Proc. SPM). 2021, 140.

Tricard, T.; Tavernier, V.; Zanni, C.; Martínez, J.; Hugron, P.-A.; Neyret, F.; Lefebvre, S. Freely orientable microstructures for designing deformable 3D prints. ACM Trans. Graph. (Proc. SIGGRAPH Asia). 2020, 39(6).

Martínez, J.; Skouras, M.; Schumacher, C.; Hornus, S.; Lefebvre, S.; Thomaszewski, B. Star-Shaped Metrics for Mechanical Metamaterial Design. ACM Trans. Graph. (Proc. SIGGRAPH). 2019, 38(4).

Martínez, J.; Hornus, S.; Song, H.; Lefebvre, S. Polyhedral Voronoi diagrams for Additive Manufacturing. ACM Trans. Graph. (Proc. SIGGRAPH). 2018, 37(4).

MuFFin aims at contributing a unified pipeline for the efficient and scalable synthesis, visualization, and modeling of additively manufactured microstructures with tailored macroscopic physical behavior. In an interdisciplinary effort, MuFFin will blend together computer and material science perspectives to deliver an integrated approach that is both computationally and physically sound.

Additive Manufacturing (AM) technologies are now capable of fabricating microstructures at the scale of microns, therefore enabling to precise control of the macroscopic physical behavior. This control empowers a wide range of industrial applications by bringing high-performance customized materials. In particular, a promising venue lies in the optimization of material properties such as rigidity or impact absorption.

Microstructures for AM will play a decisive role in the factory of the future, but several challenges remain aside. The dimension of the objects being printed increases, and concurrently, the available printing resolution becomes finer. Thus, the geometry size of microstructures is drastically escalating. From a computational viewpoint, explicitly storing the microstructure geometry (e.g in an STL file), will eventually render infeasible the whole computational pipeline (numerical simulation, visualization, and computational design of microstructures). From a material science viewpoint, it remains a challenge to properly embed and grade microstructures within an object, and to ensure that they can be directly fabricated with AM processes.

State of the art methods consider periodic microstructures, offering compact storage, efficient display, and simulation of the macroscopic physical behavior. However, due to their constrained global structure, periodic microstructures exhibit a poor grading behavior, specially when targeting anisotropic material properties that follow an arbitrary orientation field.

MuFFin seeks to answer the aforementioned interdisciplinary challenges by considering procedural, stochastic, and fabricable microstructures, with a controlled macroscopic physical behavior. Thanks to its procedural formulation, MuFFin will computationally scale with future technologies. As a result of its stochastic nature MuFFin will afford for free grading and embedding of microstructures into objects, hence avoiding the limitations imposed by periodic structures.

Project coordination

Jonàs Martínez Bayona (Centre de Recherche Inria Nancy - Grand Est)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partnership

Inria Nancy Grand Est Centre de Recherche Inria Nancy - Grand Est

Help of the ANR 236,868 euros
Beginning and duration of the scientific project: December 2017 - 42 Months

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