We propose Inkjet 4D Print, a self-folding fabrication method of 3D origami tessellations by printing 2D patterns on both sides of a heat-shrinkable base sheet, using a commercialized inkjet ultraviolet (UV) printer. Compared to the previous folding-based 4D printing approach using fused deposition modeling (FDM) 3D printers [An et al. 2018], our method has merits in (1) more than 1200 times higher resolution in terms of the number of self-foldable facets, (2) 2.8 times faster printing speed, and (3) optional full-color decoration. This paper describes the material selection, the folding mechanism, the heating condition, and the printing patterns to self-fold both known and freeform tessellations. We also evaluated the self-folding resolution, the printing and transformation speed, and the shape accuracy of our method. Finally, we demonstrated applications enabled by our self-foldable tessellated objects.

Despite the recent trend of computational origami for human-computer interaction (HCI) and digital fabrication, it is still difficult for designers to complete a series of design, simulation, and fabrication of objects leveraging computational origami theory. In this paper, we propose Crane, an integrated origami design platform implemented with Grasshopper. With this platform, users can seamlessly (1) design the 2D and 3D crease pattern, (2) simulate 3D folding transformation from the given crease pattern, (3) inversely find a new pattern under design constraints, (4) thicken the 2D pattern into a 3D volume along with the appropriate hinge structures for different fabrication methods, and (5) optionally connect the resulting design to other Rhinoceros or Grasshopper plugins for post-processes. To help understand how to use our system and demonstrate its feasibility, we showed three examples of origami products designed using our system. We also reported user feedback from the workshop as an evaluation.

We introduce a fast, robust, and user-controllable algorithm to generate surface-filling curves. We compute these curves through the gradient flow of a simple sparse energy, making our method several orders of magnitude faster than previous works. Our algorithm makes minimal assumptions on the topology and resolution of the input surface, achieving improved robustness. Our framework provides tuneable parameters that guide the shape of the output curve, making it ideal for interactive design applications.

Zippers have long been used for connecting and disconnecting two textiles repeatedly and easily. However, many industrial zippers cannot retain their shape after assembly, limiting the potential use of connecting soft textiles to form 3D shapes with large and various curvatures. Thus, we present a method to design and fabricate interlocking 3D printable zippers that can encode post-assembly shapes. The user first gives a target 3D shape divided into developable patches (i.e., curved surfaces unrollable like paper). Then, our design software built on Rhino/Grasshopper computes an interlocking zipper on the boundary curve of the patches. The user 3D prints the zipper in a flat state and welds it to the edge of the textiles by thermal bonding, which can zip into a target 3D shape. In this demo, we report our method and exhibit design examples of 3D printed zippers.

Field-aligned parametrization is a method that maps a scalar function onto a surface, such that the gradient vector of the scalar function matches the input vector field. Using this idea, one can produce a stripe pattern that is convenient for various purposes such as remeshing, texture synthesis, and computational fabrication. In the final outcome, the positions of singularities (i.e., bifurcations of the stripe pattern) are essential for functionalities, manufacturability, or aesthetics. In this paper, we propose an algorithm to allow users to interactively edit the singularity positions of field-aligned stripe patterns. The algorithm computes a stripe pattern from a prescribed set of singularities, without generating any unwanted singularities. The solution of the algorithm is formulated as the global minima of a constrained quadratic optimization, whose computation speed is dominated by solving only two sparse linear systems. Furthermore, once the two matrices in the two linear systems are factorized, any update on singularity positions operates in linear time. We showcase several applications feasible with our fast yet simple algorithm.