We present a fabrication system called AirForce that allows users to create large-scale, load-bearing animated structures from a single inflatable tube. AirForce builds on the personal fabrication of animated truss structures, based on which it replaces not only the static elements with tube, but also introduces tube-based actuators that integrate with that same tube. This ‘single-tube’ design affords efficient single-person assembly, excellent power-to-weight ratio, easy transport and setup, and 100% material reuse. We show three variants of actuators: buckling actuators for pushing, muscle actuators for pulling, and telescoping actuators for large forces. Our blender plugin enables users to place actuators in structures and export instructions for efficient fabrication. We demonstrate a 6DOF motion platform that lifts humans and an 8m high animatronic T-rex that animates with 3DOF, enabled by custom hardware components. In our technical evaluation, the three actuators delivered 480N, 1420N, and 2330N peak forces, respectively.

Laser cutting has a long tradition of building load-bearing 3D objects based on box joints and T-joints, as these joints are naturally robust against compression and shearing. Achieving robustness against tension, however, is challenging. One presumed solution is to make all joints extremely tight, to the point where they can only be assembled using a mallet. However, our survey found that making joints tight can cause models to break during assembly. In this paper, we identify the 10 underlying issues and present techniques for overcoming them: by extending parts with what we call scaffolding or by adjusting the models’ assembly order, so as to bypass states that are subject to these issues. Based on our user study and analysis of laser-cut models, scaffolding speeds up assembly for an average of 14% of the assembly operations per model, which in turn gives an average of 1.3x speed-up per model, and 70% of the models would benefit from the adjusted assembly order, that in the absence of such, would require higher assembly effort.

We present AirTied, a device that fabricates truss structures in a fully automatic fashion. AirTied achieves this by unrolling a 20cm-wide inflatable plastic tube and tying nodes into it. AirTied creates nodes by holding onto a segment of tube, stacking additional tube segments on top of it, tying them up, and releasing the result. The resulting structures are material-efficient and light as well as sturdy, as we demonstrate by creating a 6m-tower. Unlike the prior art, AirTied requires no scaffolding and no building blocks, bringing automated truss construction into the reach of personal fabrication.

We present FoolProofJoint, a software tool that simplifies the assembly of laser-cut 3D models and reduces the risk of erroneous assembly. FoolProofJoint achieves this by modifying finger joint patterns. Wherever possible, FoolProofJoint makes similar looking pieces fully interchangeable, thereby speeding up the user's visual search for a matching piece. When that is not possible, FoolProofJoint gives finger joints a unique pattern of individual finger placements so as to fit only with the correct piece, thereby preventing erroneous assembly. In our benchmark set of 217 laser-cut 3D models downloaded from kyub.com, FoolProofJoint made groups of similar looking pieces fully interchangeable for 65% of all groups of similar pieces; FoolProofJoint fully prevented assembly mistakes for 97% of all models.

We present HingeCore, a novel type of laser-cut 3D structure made from sandwich materials, such as foamcore. The key design element behind HingeCore is what we call a finger hinge, which we produce by laser-cutting foamcore “half-way”. The primary benefit of finger hinges is that they allow for very fast assembly, as they allow models to be assembled by folding and because folded hinges stay put at the intended angle, based on the friction between fingers alone, which eliminates the need for glue or tabs. Finger hinges are also highly robust, with some 5mm foamcore models withstanding 62kg. We present HingeCoreMaker, a stand-alone software tool that automatically converts 3D models to HingeCore layouts, as well as an integration into a 3D modeling tool for laser cutting (Kyub [7]). We have used HingeCoreMaker to fabricate design objects, including speakers, lamps, and a life-size bust, as well as structural objects, such as functional furniture. In our user study, participants assembled HingeCore layouts 2.9x faster than layouts generated using the state-of-the-art for plate-based assembly (Roadkill [1]).

We present Assembler3 a software tool that allows users to perform 3D parametric manipulations on 2D laser cutting plans. Assembler3 achieves this by semi-automatically converting 2D laser cutting plans to 3D, where users modify their models using available 3D tools (kyub), before converting them back to 2D. In our user study, this workflow allowed users to modify models 10x faster than using the traditional approach of editing 2D cutting plans directly. Assembler3 converts models to 3D in 5 steps: (1) plate detection, (2) joint detection, (3) material thickness detection, (4) joint matching based on hashed joint "signatures", and (5) interactive reconstruction. In our technical evaluation, Assembler3 was able to reconstruct 100 of 105 models. Once 3D-reconstructed, we expect users to store and share their models in 3D, which can simplify collaboration and thereby empower the laser cutting community to create models of higher complexity.

Recent research showed how to import laser cut 3D models encoded in the form of 2D cutting plans into a 3D editor (assembler3 [28]), which allows users to perform parametric manipulations on such models. In contrast to assembler3 , which requires users to perform this process manually, we present autoAssembler, which performs this process automatically. AutoAssembler uses a beam search algorithm to search possible ways of assembling plates. It uses joints on these plates to combine them into assembly candidates. It thereby preferably pursues candidates (1) that have no intersecting plates, (2) that fit into a small bounding box, (3) that use plates whose joints fit together well, (4) that do not add many unpaired joints, (5) that make use of constraints posed by other plates, and (6) that conform to symmetry axes of the plates. This works for models that have at least one edge joint (finger or t-joint). In our technical evaluation, we imported 66 models using autoAssembler. AutoAssembler assembled 79% of those models fully automatically; another 18% of models required on average 2.7 clicks of post-processing, for an overall success rate of 97%.

Machine learning is rapidly becoming a mainstay in research and industry. Particularly for clinical predictive modeling, these approaches are being increasingly applied, as evidenced by the growth in the number of related publications. While different computer tools exist that support rapid prototyping, we observe that the state of the art is lacking in the extent to which the needs of research clinicians are addressed. This leads to an increase in the time needed for development and validation of such models. In this paper, we outline the requirements and challenges inherent to this domain and present a platform for rapid prototyping tailored to the specific needs of clinical modeling for outcome and risk prediction. We argue that a move towards hybrid solutions, i.e., a mix of cloud and on-premise infrastructure, constitutes a viable way to reduce the time needed to develop and validate clinical predictive models in a standardized, reproducible fashion.