Material Extrusion (MEX) systems with dual-material capability can unlock interesting applications where flexible and rigid materials are combined. When chemically incompatible materials are concerned the adhesion between the two might be insufficient. Therefore researchers typically rely on dovetail type interlocking geometries in order to affix two bodies mechanically. However, dovetail type interlocking introduces extrusion discontinuities and relies on the material’s resistance to deformation, which is difficult to model. We propose a simple and effective 3D lattice consisting of interlaced horizontal beams in vertically alternating directions which interlock topologically: the interlaced topologically interlocking lattice (ITIL). It ensures continuous extrusion and ensures an interlock even for highly flexible materials. We develop analytical models for optimizing the ultimate tensile strength of the ITIL lattice in two different orientations relative to the interface: straight and diagonal. The analytical models are applied to polypropylene (PP) and polylactic acid (PLA) and verified by finite elements method (FEM) simulations and physical tensile experiments. In the diagonal orientation ITIL can obtain 82% of the theoretical upper bound of 8.6 MPa. ITIL seems to perform comparably to dovetail interlocking designs, while it lends itself to application to non-vertical interfaces. Optimizing the lattice for non-vertical interfaces, however, remains future work.@en

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Real-time proprioception is a challenging problem for soft robots, which have virtually infinite degrees of freedom in body deformation. When multiple actuators are used, it becomes more difficult as deformation can also occur on actuators caused by interaction between each other. To tackle this problem, we present a method in this article to sense and reconstruct 3-D deformation on pneumatic soft robots by first integrating multiple low-cost sensors inside the chambers of pneumatic actuators and then using machine learning to convert the captured signals into shape parameters of soft robots. An exterior motion capture system is employed to generate the datasets for both training and testing. With the help of good shape parameterization, the 3-D shape of a soft robot can be accurately reconstructed from signals obtained from multiple sensors. We demonstrate the effectiveness of this approach on two soft robot designs - a robotic joint and a deformable membrane. After parameterizing the deformation of these soft robots into compact shape parameters, we can effectively train the neural networks to reconstruct the 3-D deformation from the sensor signals. The sensing and shape prediction pipeline can run at 50 Hz in real time on a consumer-level device.

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Lattice structures have been widely used in various applications of additive manufacturing due to its superior physical properties. If modeled by triangular meshes, a lattice structure with huge number of struts would consume massive memory. This hinders the use of lattice structures in large-scale applications (e.g., to design the interior structure of a solid with spatially graded material properties). To solve this issue, we propose a memory-efficient method for the modeling and slicing of adaptive lattice structures. A lattice structure is represented by a weighted graph where the edge weights store the struts' radii. When slicing the structure, its solid model is locally evaluated through convolution surfaces in a streaming manner. As such, only limited memory is needed to generate the toolpaths of fabrication. Also, the use of convolution surfaces leads to natural blending at intersections of struts, which can avoid the stress concentration at these regions. We also present a computational framework for optimizing supporting structures and adapting lattice structures with prescribed density distributions. The presented methods have been validated by a series of case studies with large number (up to 100 M) of struts to demonstrate its applicability to large-scale lattice structures.

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