Elastic textures for additive fabrication

Panetta, Julian; Zhou, Qun; Malomo, Luigi; Pietroni, Nico; Cignoni, Paolo; Zorin, Denis

doi:10.1145/2766937

Cited by 248 publications

(189 citation statements)

References 43 publications

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“…Both the model and the applications are evaluated in a series of user experiments. The proposed model can be easily integrated in many existing computational fabrication algorithms, such as [Bickel et al 2010;Chen et al 2013;Schumacher et al 2015;Panetta et al 2015].…”

Section: Resultsmentioning

confidence: 99%

“…Recent work tackles this problem by fabricating metamaterials made of micro-structures [Bickel et al 2010;Schumacher et al 2015;Panetta et al 2015]. However, due to the limitations imposed by designers or 3D printers, complex internal structures are not always desirable or fabricable.…”

Section: Accuracymentioning

confidence: 99%

“…Previous work [Bickel et al 2010;Schumacher et al 2015;Panetta et al 2015] has demonstrated that, by varying the internal structure of an object, so-called metamaterials can be created. These metamaterials increase the gamut of printable, non-linearly elastic materials available.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An interaction-aware, perceptual model for non-linear elastic objects

Piovarči

Levin

Rebello³

et al. 2016

ACM Trans. Graph.

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Force-displacement Force histogramPhysically based simulation Stiff Soft Figure 1: 3D printing allows us to print objects with varying deformation properties. The question that we want to answer is: Given a set of printing materials and a 3D object with desired elasticity properties, which material should be used to print the object? For example, given sample ducks (left) with desired elasticity properties (e.g., measured), our system considers several candidate materials that can be used for replicating the ducks (right), and chooses materials that will best match compliance properties when examined by an observer (red and green outlines). Moreover, we can sort all possible materials by their perceived compliance as predicted by our model. The measured compliance is indicated with colors ranging from stiff (blue) to soft (red). AbstractEveryone, from a shopper buying shoes to a doctor palpating a growth, uses their sense of touch to learn about the world. 3D printing is a powerful technology because it gives us the ability to control the haptic impression an object creates. This is critical for both replicating existing, real-world constructs and designing novel ones. However, each 3D printer has different capabilities and supports different materials, leaving us to ask: How can we best replicate a given haptic result on a particular output device? In this work, we address the problem of mapping a real-world material to its nearest 3D printable counterpart by constructing a perceptual model for the compliance of nonlinearly elastic objects. We begin by building a perceptual space from experimentally obtained user comparisons of twelve 3D-printed metamaterials. By comparing this space to a number of hypothetical computational models, we identify those that can be used to accurately and efficiently evaluate human-perceived differences in nonlinear stiffness. Furthermore, we demonstrate how such models can be applied to complex geometries in an interaction-aware way where the compliance is influenced not only by the material properties from which the object is made but also its geometry. We demonstrate several applications of our method in the context of fabrication and evaluate them in a series of user experiments.

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Section: Resultsmentioning

confidence: 99%

Section: Accuracymentioning

confidence: 99%

See 1 more Smart Citation

An interaction-aware, perceptual model for non-linear elastic objects

Piovarči

Levin

Rebello³

et al. 2016

ACM Trans. Graph.

View full text Add to dashboard Cite

show abstract

“…is approach, based on the homogenization theory, is a common practice and has been widely used in the past [Allaire 2012;Pane a et al 2015;Schumacher et al 2015]. However, while inferring the homogenized properties of individual microstructures is not particularly challenging, analyzing the space covered by all combinations of base materials is much more di cult due to the combinatorial explosion in the number of possible material arrangements.…”

Section: Materials Space Explorationmentioning

confidence: 99%

Two-Scale Topology Optimization with Microstructures

Zhu¹,

Skouras²,

Chen³

et al. 2017

ACM Trans. Graph.

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Fig. 1. Our two-scale topology optimization framework allows to optimize continuous material properties mapping to printable microstructures (le ) to fabricate high-resolution functional objects (middle) and minimum compliant structures (right).In this paper we present a novel two-scale framework to optimize the structure and the material distribution of an object given its functional specications. Our approach utilizes multi-material microstructures as low-level building blocks of the object. We start by precomputing the material property gamut -the set of bulk material properties that can be achieved with all material microstructures of a given size. We represent the boundary of this material property gamut using a level set eld. Next, we propose an e cient and general topology optimization algorithm that simultaneously computes an optimal object topology and spatially-varying material properties constrained by the precomputed gamut. Finally, we map the optimal spatially-varying material properties onto the microstructures with the corresponding properties in order to generate a high-resolution printable structure. We demonstrate the e cacy of our framework by designing, optimizing, and fabricating objects in di erent material property spaces on the level of a trillion voxels, i.e several orders of magnitude higher than what can be achieved with current systems.

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“…Recent work tries to generate micro-structures: Dumas et al [10] use exemplar-based synthesis to generate structural patterns. Both Panetta et al [27] and Schumacher et al [33] optimize the material micro-structure and its tiling to achieve elastic properties. Our system supports tiled solid textures in addition to procedural solid textures.…”

Section: Data-driven Solid Texture Synthesismentioning

confidence: 99%

Foundry

Vidimče

Kaspar

Wang

et al. 2016

Proceedings of the 29th Annual Symposium on User Interface Software and Technology

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Figure 1. Objects designed using Foundry. Left: a paddle with a perforated blade and a 'sweet' spot on its smooth side. Center: a simulation-driven ski design with retro-reflective surfaces. Right: a trycycle 'tweel' with lattice spokes for lateral strength and foam for improved suspension. ABSTRACTWe demonstrate a new approach for designing functional material definitions for multi-material fabrication using our system called Foundry. Foundry provides an interactive and visual process for hierarchically designing spatially-varying material properties (e.g., appearance, mechanical, optical). The resulting meta-materials exhibit structure at the micro and macro level and can surpass the qualities of traditional composites. The material definitions are created by composing a set of operators into an operator graph. Each operator performs a volume decomposition operation, remaps space, or constructs and assigns a material composition. The operators are implemented using a domain-specific language for multi-material fabrication; users can easily extend the library by writing their own operators. Foundry can be used to build operator graphs that describe complex, parameterized, resolution-independent, and reusable material definitions. We also describe how to stage the evaluation of the final material definition which in conjunction with progressive refinement, allows for interactive material evaluation even for complex designs. We show sophisticated and functional parts designed with our system.

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Elastic textures for additive fabrication

Cited by 248 publications

References 43 publications

An interaction-aware, perceptual model for non-linear elastic objects

An interaction-aware, perceptual model for non-linear elastic objects

Two-Scale Topology Optimization with Microstructures

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