No abstract

Low-fidelity fabrication systems speed up rapid prototyping by printing intermediate versions of a prototype as fast, low-fidelity previews. Only the final version is fabricated as a full high-fidelity 3D print. This allows designers to iterate more quickly-achieving a better design in less time. Depending on what is currently being tested, low-fidelity fabrication is implemented in different ways: (1) faBrickator allows for a modular approach by substituting sub-volumes of the 3D model with building blocks. (2) WirePrint allows for quickly testing the shape of an object, such as the ergonomic fit, by printing wireframe structures. (3) Platener preserves the technical function by substituting 3D print with laser-cut plates of the same size and thickness.At our CHI'15 interactivity booth, we give a combined live demo of all three low-fidelity fabrication systemsputting special focus on our new low-fidelity fabrication system Platener (paper at CHI'15).

We present a new approach to rapid prototyping of functional objects, such as the body of a head-mounted display. The key idea is to save 3D printing time by automatically substituting sub-volumes with standard building blocks-in our case Lego bricks. When making the body for a head-mounted display, for example, getting the optical path right is paramount. Users thus mark the lens mounts as "high-resolution" to indicate that these should later be 3D printed. faBrickator then 3D prints these parts. It also generates instructions that show users how to create everything else from Lego bricks. If users iterate on the design later, faBrickator offers even greater benefit as it allows reprinting only the elements that changed. We validated our system at the example of three 3D models of functional objects. On average, our system fabricates objects 2.44 times faster than traditional 3D printing while requiring only 14 minutes of manual assembly. Figure 1: Let's fabricate this head-mounted display body quickly: (a) The exact shape of the lens mounts matters; the user thus (b) marks them as "high-resolution" in faBrickator and 3D prints them. (c) faBrickator shows the user how to create everything else from Lego bricks and how to insert the 3D printed part. Done in 67 minutes instead of the 14:30h for 3D printing.

We present a new approach to rapid prototyping of functional objects, such as the body of a head-mounted display. The key idea is to save 3D printing time by automatically substituting sub-volumes with standard building blocks -in our case Lego bricks. When making the body for a head-mounted display, for example, getting the optical path right is paramount. Users thus mark the lens mounts as "high-resolution" to indicate that these should be 3D printed. faBrickator then 3D-prints only these parts. It also generates instructions that show users how to create everything else from Lego bricks. If users iterate on the design later, faBrickator offers even greater benefit as it allows reprinting only the elements that changed. We validated our system at the example of three 3D models of functional objects. On average, our system fabricates objects 2.44 times faster than traditional 3d printing while requiring only 14 minutes of manual assembly.

Modern production of fiber reinforced composites via the preforming process is widely used in the industry. A common way to create dry, semi-finished fiber products is forming or draping a textile into a three-dimensional component geometry. The punch and die process is often used for resin transfer molding (RTM) composite manufacturing. Due to the major influence of the preforming step on the later mechanical performance of the component, a detailed knowledge of the fiber architecture is beneficial.To enable in-situ monitoring of the specific deformation of a woven fabric, a novel kind of single-use two-dimensional strain sensors has already been developed and characterized. We show that by using industrial communication standards, data from various data sources can be consolidated in an edge computer and used to improve the process. To this end, we developed the hardware and firmware of a device that reads out the printed strain sensors and transfers the data to the edge device via IO-Link. In addition, the edge device collects data from a programmable logic controller and is capable of connecting further IO-Link sensors.Our demonstrator is intended as a proof of concept for in-situ monitoring, data-driven analysis and improvement of the punch and die process and will be further developed. We propose a machine learning-based edge analytics approach for detecting defects and increasing the preforming quality during the draping process. Forming tests with the double-dome benchmark geometry and the carbon fabric which is suitable for industry have been carried out to validate our prototype system.