Spec2Fab: A reducer-tuner model for translating specifications to 3D prints

Multi-material 3D printing allows objects to be composed of complex, heterogenous arrangements of materials. It is often more natural to define a functional goal than to define the material composition of an object. Translating these functional requirements to fabri-cable 3D prints is still an open research problem. Recently, several specific instances of this problem have been explored (e.g., appearance or elastic deformation), but they exist as isolated, monolithic algorithms. In this paper, we propose an abstraction mechanism that simplifies the design, development, implementation, and reuse of these algorithms. Our solution relies on two new data structures: a reducer tree that efficiently parameterizes the space of material assignments and a tuner network that describes the optimization process used to compute material arrangement. We provide an application programming interface for specifying the desired object and for defining parameters for the reducer tree and tuner network. We illustrate the utility of our framework by implementing several fabrication algorithms as well as demonstrating the manufactured results.United States. Defense Advanced Research Projects Agency (N66001-12-1-4242)National Science Foundation (U.S.) (CCF-1138967)reducer-tuner model for translating specifications to 3D prints (IIS-1116296)Google (Firm) (Faculty Research Award

Joint view expansion and filtering for automultiscopic 3D displays

Multi-view autostereoscopic displays provide an immersive, glasses-free 3D viewing experience, but they require correctly filtered content from multiple viewpoints. This, however, cannot be easily obtained with current stereoscopic production pipelines. We provide a practical solution that takes a stereoscopic video as an input and converts it to multi-view and filtered video streams that can be used to drive multi-view autostereoscopic displays. The method combines a phase-based video magnification and an interperspective antialiasing into a single filtering process. The whole algorithm is simple and can be efficiently implemented on current GPUs to yield a near real-time performance. Furthermore, the ability to retarget disparity is naturally supported. Our method is robust and works well for challenging video scenes with defocus blur, motion blur, transparent materials, and specularities. We show that our results are superior when compared to the state-of-the-art depth-based rendering methods. Finally, we showcase the method in the context of a real-time 3D videoconferencing system that requires only two cameras.Quanta Computer (Firm)National Science Foundation (U.S.) (NSF IIS-1111415)National Science Foundation (U.S.) (NSF IIS-1116296

MultiFab: a machine vision assisted platform for multi-material 3D printing

We have developed a multi-material 3D printing platform that is high-resolution, low-cost, and extensible. The key part of our platform is an integrated machine vision system. This system allows for self-calibration of printheads, 3D scanning, and a closed-feedback loop to enable print corrections. The integration of machine vision with 3D printing simplifies the overall platform design and enables new applications such as 3D printing over auxiliary parts. Furthermore, our platform dramatically expands the range of parts that can be 3D printed by simultaneously supporting up to 10 different materials that can interact optically and mechanically. The platform achieves a resolution of at least 40 μm by utilizing piezoelectric inkjet printheads adapted for 3D printing. The hardware is low cost (less than $7,000) since it is built exclusively from off-the-shelf components. The architecture is extensible and modular -- adding, removing, and exchanging printing modules can be done quickly. We provide a detailed analysis of the system's performance. We also demonstrate a variety of fabricated multi-material objects.National Science Foundation (U.S.) (Grant CCF-1138967)United States. Defense Advanced Research Projects Agency (Grant N66001-12-1-4242

Exploring the Impact of Normality and Significance Tests in Architecture Experiments ABSTRACT

Computer architects often use statistical tools such as means in reporting results. While there has been discussion regarding which means to use for different metrics, the impact of the underlying assumptions involved in reporting results in the architecture community has been largely unexplored. This paper investigates the validity of assumptions such as the normality of the data gathered and the use of significance tests. These are demonstrated on actual branch predictor experiments using the SPECcpu2000 benchmark suite. We find our measures (IPC, branch misprediction, correct branch direction, and correct branch address rates) are mostly normally distributed in these experiments. Through the use of additional statistical tests, we also illustrate that simple visual inspection of results can be misleading, implying differences where no statistical difference exists or disguising a difference that is important. 1

Design and fabrication by example

We propose a data-driven method for designing 3D models that can be fabricated. First, our approach converts a collection of expert-created designs to a dataset of parameterized design templates that includes all information necessary for fabrication. The templates are then used in an interactive design system to create new fabri-cable models in a design-by-example manner. A simple interface allows novice users to choose template parts from the database, change their parameters, and combine them to create new models. Using the information in the template database, the system can automatically position, align, and connect parts: the system accomplishes this by adjusting parameters, adding appropriate constraints, and assigning connectors. This process ensures that the created models can be fabricated, saves the user from many tedious but necessary tasks, and makes it possible for non-experts to design and create actual physical objects. To demonstrate our data-driven method, we present several examples of complex functional objects that we designed and manufactured using our system.National Science Foundation (U.S.) (Grant 1138967

Design and fabrication by example

We propose a data-driven method for designing 3D models that can be fabricated. First, our approach converts a collection of expert-created designs to a dataset of parameterized design templates that includes all information necessary for fabrication. The templates are then used in an interactive design system to create new fabri-cable models in a design-by-example manner. A simple interface allows novice users to choose template parts from the database, change their parameters, and combine them to create new models. Using the information in the template database, the system can automatically position, align, and connect parts: the system accomplishes this by adjusting parameters, adding appropriate constraints, and assigning connectors. This process ensures that the created models can be fabricated, saves the user from many tedious but necessary tasks, and makes it possible for non-experts to design and create actual physical objects. To demonstrate our data-driven method, we present several examples of complex functional objects that we designed and manufactured using our system.National Science Foundation (U.S.) (Grant 1138967

Alignment Control of Ferrite-Decorated Nanocarbon Material for 3D Printing

This paper demonstrates the potential of anisotropic 3D printing for alignable carbon nanomaterials. The ferrite-decorated nanocarbon material was synthesized via a sodium solvation process using epichlorohydrin as the coupling agent. Employing a one-pot synthesis approach, the novel material was incorporated into a 3D photopolymer, manipulated, and printed using a low-cost microscale 3D printer, equipped with digital micromirror lithography, monitoring optics, and magnetic actuators. This technique highlights the ability to control the microstructure of 3D-printed objects with sub-micron precision for applications such as microelectrode sensors and microrobot fabrication