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MATERIALIZABLE: Intelligent fabrication-oriented Computational Design and Modeling

Periodic Reporting for period 3 - MATERIALIZABLE (MATERIALIZABLE: Intelligent fabrication-oriented Computational Design and Modeling)

Reporting period: 2020-02-01 to 2021-07-31

While access to 3D-printing technology becomes ubiquitous and provides revolutionary possibilities for fabricating complex, functional, multi-material objects with stunning properties, its potential impact is currently significantly limited due to the lack of efficient and intuitive methods for content creation. Existing tools are usually restricted to expert users, have been developed based on the capabilities of traditional manufacturing processes, and do not sufficiently take fabrication constraints into account. Scientifically, we are facing the fundamental challenge that existing simulation techniques and design approaches for predicting the physical properties of materials and objects at the resolution of modern 3D printers are too slow and do not scale with increasing object complexity. The problem is extremely challenging because real world-materials exhibit extraordinary variety and complexity.

To address these challenges, this project suggest a novel computational approach that facilitates intuitive design, accurate and fast simulation techniques, and a functional representation of 3D content. We propose a multi-scale representation of functional goals and hybrid models that describes the physical behavior at a coarse scale and the relationship to the underlying material composition at the resolution of the 3D printer. Our approach is to combine data-driven and physically-based modeling, providing both the required speed and accuracy through smart precomputations and tailored simulation techniques that operate on the data. Subsequently, we propose the fundamental re-thinking of the workflow, leading to solutions that allow synthesizing model instances optimized on-the-fly for a specific output device. The principal applicability will be evaluated for functional goals, such as appearance, deformation, and sensing capabilities.

Linking computation, data, and new digital fabrication technologies may have as profound an impact on the world as the coming of the factory did. New theoretical insight and practical algorithms that extend the traditional content creation pipeline of computer graphics and computer-aided engineering will fundamentally change how functional objects are designed and personalized. Related areas such as robotics, industrial design, architecture, consumer goods creation, and bio printing will benefit because complex designs can be realized more quickly, more accurately, and in a way that was previously impossible. Due to this development, new business opportunities will evolve.
WP1) Modeling, simulation, and reproduction of appearance

We made significant progress towards a computationally efficient model that allows us to simulate the appearance by taking into account lower-scale material phenomena. In contrast to pigment-based colors – where spectral components of incident light are absorbed by the pigment material – structural colorization arises from the interaction of light with micro- and nanostructures. Designing such colorization is challenging, however, since the wave nature of light has to be taken into account when simulating its interplay with nanostructures. In the work of Auzinger et al. 2018, we present a computational design tool for the creation of transparent nanostructures with feature sizes in the range of 100s of nm that realize simple colorization of light transmitted through them. The system is built around a full electromagnetic simulation of light. We take fabrication constraints of multiphoton lithography into account to ensure the feasibility of fabricating the designs.

On a larger scale, we also investigated the reproduction of colored textured objects using polyjet 3D printing technology. Color texture reproduction in 3D printing commonly ignores volumetric light transport (cross-talk) between surface points on a 3D print. Such light diffusion leads to significant blur of details and color bleeding, and is particularly severe for highly translucent resin-based print materials. In Elek et al. 2021, we presented a robust and practical measurement system for the materials light transport parameters. We counteract heterogeneous scattering to obtain the impression of a crisp albedo texture on top of the 3D print, by optimizing for a fully volumetric material distribution that preserves the target appearance (Elek et al. 2017). We evaluated our system using a commercial, five-tone 3D print process, demonstrating that our method preserves high-frequency features well without having to compromise on color gamut. In the follow-up work of Sumin et al. 2019, we designed a full-fledged optimization-based heuristic for arbitrary 3D shapes. Our method enables high-fidelity color texture reproduction on 3D prints by effectively compensating for internal light scattering within arbitrarily shaped objects. However, this optimization-based approach is computationally expensive, taking in the order of days in a single machine. In Rittig et al. 2021, we replace the light transport simulation with a data-driven approach, allowing a speed up of two orders of magnitude while achieving results of similar quality.


WP2) Modeling, simulation, and reproduction of elastic behaviour

Many functional objects in our everyday lives consist of elastic, deformable material, and the material properties are often inextricably linked to function. We made important steps towards modeling systems for designing large, heterogeneous elastic objects with higher-level functional behavior.

FlexMaps (Malomo et al. 2018) is a novel framework we developed for fabricating smooth shapes out of flat, flexible panels with tailored mechanical properties. For these panels, we design and obtain specific mechanical properties such that, once they are assembled, the static equilibrium configuration matches the desired 3D shape. Similarly, we also studied the design space of plane elastic curves (Hafner et al. 2021) and cold bent glass panels (Gavriil et al. 2020). Closely connected to this work is self-actuated material and structure design. These types of structures are usually composed of an actuation mechanism and a deformation limiting mechanism that, when coupled together, produce the desired deformed shape. We have developed a computational approach for designing curvy shells that self-actuate from an initially flat state (Guseinov et al. 2020).

For reproducing digital objects, we developed a new technique through reusable elastic molds (Alderighi et al. 2018). The molds are generated by casting liquid silicone into custom 3D printed containers called metamolds. Metamolds automatically define the cuts that are needed to extract the cast object from the silicone mold. Gaining a deeper understanding of the problem, we realized an innovative formulation for how to place cuts in the mold volume to allow for cast extraction (Alderighi et al. 2019). We also introduced a novel technique to automatically decompose an input object's volume into a set of parts that can be represented by two opposite height fields, enabling the manufacturing of individual parts using two-piece reusable rigid molds (Alderighi et al. 2021)

We also recently have developed a general method for shape optimization of models that directly works on CAD model representations and allows to handle complex geometry without the need of remeshing thanks to an underlying XFFEM approach (Hafner et al 2019).

WP 3) Modeling, simulation, and the reproduction of sensing


Tactile feedback of an object’s surface enables us to discern its material properties and affordances. This understanding is used in digital fabrication processes by creating objects with high-resolution surface variations to influence a user’s tactile perception. As the design of such surface haptics commonly relies on knowledge from real-life experiences, it is unclear how to adapt this information for digital design methods. In Degraen et al. 2021, we investigate replicating the haptics of real materials and propose strategies for capturing and reproducing digitized textures to better resemble the perceived haptics of the originals.

WP 4) Unified Simulation Framework

In this WP our goal is to develop a framework for multi-objective optimization and efficient design exploration. Working toward this goal, we developed a data-driven technique to instantly predict how fluid flows around various three-dimensional objects (Umetani et al. 2018). Such simulation is useful for computational fabrication and engineering, but is usually computationally expensive since it requires solving the Navier-Stokes equation for many time steps. We demonstrate the effectiveness of our approach for the interactive design and optimization of a car body. Furthermore, we investigated the interactive design of functional mechanical objects that can be 3D printed (Zhang et al. 2017). Our computational approach allows novice users to retarget an existing mechanical template to a user-specified input shape. Finally, we made significant progress towards more efficiently reproducing an objects shape based on traditional molding (Nakashima et la. 2018, Alderighi et al. 2021). Our technique enables a novel workflow, as it empowers novice users to explore the design space, and it generates fabrication-ready two-piece molds that can be used either for casting or industrial injection molding of free-form objects.
The main scientific result will be solid theoretical insights and novel algorithms that facilitate intelligent fabrication-oriented computational design and modeling. These will enable a new stage for functional digital content creation for 3D printers. The Materializable project will have significant impacts at several levels scientifically and economically. By closing the loop between the real and virtual world, we will be able to scientifically evaluate and improve the models that are currently used in simulation. This will lead to novel, accurate data-driven models that are applicable for analyzing, simulating, and solving various kinds of scientific and engineering problems, thereby having a wide scientific impact on the computer graphics community and beyond. Our theoretical insights and algorithmic foundations for representing functional models, design spaces, and the gamut of output devices will leverage the area of intuitive, functional content creation to a new stage, and we expect that the computer graphics, geometric modeling and processing, and science and engineering community will build future research on this fundament. By disseminating our results in an open-source framework, we will support the scientific and engineering community in this endeavor.
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