Towards Zero-Waste Furniture Design

In traditional design, shapes are first conceived, and then fabricated. While this decoupling simplifies the design process, it can result in inefficient material usage, especially where off-cut pieces are hard to reuse. The designer, in absence of explicit feedback on material usage remains helpless to effectively adapt the design -- even though design variabilities exist. In this paper, we investigate {\em waste minimizing furniture design} wherein based on the current design, the user is presented with design variations that result in more effective usage of materials. Technically, we dynamically analyze material space layout to determine {\em which} parts to change and {\em how}, while maintaining original design intent specified in the form of design constraints. We evaluate the approach on simple and complex furniture design scenarios, and demonstrate effective material usage that is difficult, if not impossible, to achieve without computational support

Stability-aware simplification of curve networks

La conception de réseaux de courbes nécessite la considération de plusieurs facteurs: la stabilité de la structure, l'efficience matérielle, et l'aspect esthétique - des objectifs complexes et interdépendants rendant la conception manuelle difficile. Nous présentons une nouvelle méthode permettant de simplifier des réseaux de courbes destinés à la fabrication. Pour un ensemble de courbes 3D donné, notre algorithme en sélectionne un sous-ensemble stable. Bien que la stabilité soit traditionnellement mesurée par l'ordre de grandeur des déformations entraînées par des charges prédéfinies, une telle approche peut s'avérer limitante. Elle ne tient ni compte des effets de vibration pour les structures de grandes tailles, ni des multiples possibilités de forces appliquées pour les structures et objets de plus petite taille. Ainsi, nous optimisons directement pour une déformation minimale avec la charge dans le pire des cas (de l'anglais "worst-case"). Notre contribution technique est une nouvelle formulation de la simplification de réseaux de courbes pour la stabilité dans le pire des cas. Celle-ci mène à un problème d'optimisation semi-définie positive en nombres entiers (MI-SDP). Malgré que résoudre ce problème MI-SDP directement est irréaliste dans la plupart des cas, une intuition physique nous mène à un algorithme vorace efficace. Enfin, nous démontrons le potentiel de notre approache à l'aide plusieurs réseaux de courbes et validons l'efficacité de notre méthode en la comparant de façon quantitative à des approaches plus simples.Designing curve networks for fabrication requires simultaneous consideration of structural stability, cost effectiveness, and visual appeal - complex, interrelated objectives that make manual design a difficult and tedious task. We present a novel method for fabrication-aware simplification of curve networks, algorithmically selecting a stable subset of given 3D curves. While traditionally, stability is measured as the magnitude of deformation induced by a set of predefined loads, predicting applied forces for common day objects can be challenging. Instead, we directly optimize for minimal deformation under the worst-case load. Our technical contribution is a novel formulation of 3D curve network simplification for worst-case stability, leading to a mixed-integer semi-definite programming problem (MI-SDP). We show that while solving MI-SDP directly is impractical, a physical insight suggests an efficient greedy heuristic algorithm. We demonstrate the potential of our approach on a variety of curve network designs and validate its effectiveness compared to simpler alternatives using numerical experiments

From 3D Models to 3D Prints: an Overview of the Processing Pipeline

Due to the wide diffusion of 3D printing technologies, geometric algorithms for Additive Manufacturing are being invented at an impressive speed. Each single step, in particular along the Process Planning pipeline, can now count on dozens of methods that prepare the 3D model for fabrication, while analysing and optimizing geometry and machine instructions for various objectives. This report provides a classification of this huge state of the art, and elicits the relation between each single algorithm and a list of desirable objectives during Process Planning. The objectives themselves are listed and discussed, along with possible needs for tradeoffs. Additive Manufacturing technologies are broadly categorized to explicitly relate classes of devices and supported features. Finally, this report offers an analysis of the state of the art while discussing open and challenging problems from both an academic and an industrial perspective.Comment: European Union (EU); Horizon 2020; H2020-FoF-2015; RIA - Research and Innovation action; Grant agreement N. 68044

Manufacturing Metallic Parts with Designed Mesostructure via Three-Dimensional Printing of Metal Oxide Powder

Cellular materials, metallic bodies with gaseous voids, are a promising class of materials that offer high strength accompanied by a relatively low mass. In this paper, the authors investigate the use of ThreeDimensional Printing (3DP) to manufacture metallic cellular materials by selectively printing binder into a bed of metal oxide ceramic powder. The resulting green part undergoes a thermal chemical post-process in order to convert it to metal. As a result of their investigation, the authors are able to create cellular materials made of maraging steel that feature wall sizes as small as 400 µm and angled trusses and channels that are 1 mm in diameter.Mechanical Engineerin

Learning Material-Aware Local Descriptors for 3D Shapes

Material understanding is critical for design, geometric modeling, and analysis of functional objects. We enable material-aware 3D shape analysis by employing a projective convolutional neural network architecture to learn material- aware descriptors from view-based representations of 3D points for point-wise material classification or material- aware retrieval. Unfortunately, only a small fraction of shapes in 3D repositories are labeled with physical mate- rials, posing a challenge for learning methods. To address this challenge, we crowdsource a dataset of 3080 3D shapes with part-wise material labels. We focus on furniture models which exhibit interesting structure and material variabil- ity. In addition, we also contribute a high-quality expert- labeled benchmark of 115 shapes from Herman-Miller and IKEA for evaluation. We further apply a mesh-aware con- ditional random field, which incorporates rotational and reflective symmetries, to smooth our local material predic- tions across neighboring surface patches. We demonstrate the effectiveness of our learned descriptors for automatic texturing, material-aware retrieval, and physical simulation. The dataset and code will be publicly available.Comment: 3DV 201

Designing Volumetric Truss Structures

We present the first algorithm for designing volumetric Michell Trusses. Our method uses a parametrization approach to generate trusses made of structural elements aligned with the primary direction of an object's stress field. Such trusses exhibit high strength-to-weight ratios. We demonstrate the structural robustness of our designs via a posteriori physical simulation. We believe our algorithm serves as an important complement to existing structural optimization tools and as a novel standalone design tool itself