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MATERIALIZABLE: Intelligent fabrication-oriented Computational Design and Modeling

Periodic Reporting for period 4 - MATERIALIZABLE (MATERIALIZABLE: Intelligent fabrication-oriented Computational Design and Modeling)

Reporting period: 2021-08-01 to 2022-01-31

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.

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.

We made significant progress towards efficiently simulating the appearance of an object 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. 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. We take fabrication constraints of multiphoton lithography into account to ensure the feasibility of fabricating the designs.

We also investigated the reproduction of colored textured objects using polyjet 3D printing technology. Color texture reproduction in 3D printing is affected by volumetric light transport (cross-talk) between surface points on a 3D print, which can lead to significant blur of details and color bleeding. In Elek et al. 2021, we presented a 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 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, and 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.

Elastic behaviour

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. 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 for reusable elastic mold design (Alderighi et al. 2018). We realized an innovative formulation for how to place cuts in the mold volume to allow for cast extraction (Alderighi et al. 2019) and enabling the manufacturing of individual parts using two-piece reusable rigid molds (Alderighi et al. 2021).

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


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.

Unified Simulation

Our goal was to develop a framework for multi-objective optimization and efficient design exploration. We developed a data-driven technique to instantly predict how fluid flows around various three-dimensional objects (Umetani et al. 2018). 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). We also made important progress towards an active control system for manufacturing with self-correcting behavior, employing reinforcement learning to dynamically adjusts the printing path and parameters.
The main scientific result are solid theoretical insights and novel algorithms that facilitate intelligent fabrication-oriented computational design and modeling. These have enabled 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 have developed 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. We expect that the computer graphics, geometric modeling and processing, and science and engineering community will build future research on this fundament.
Spatio-temporally Programmed Shell