Arbitrary 3D Shapes Research Articles

A rapid and low-cost method to fabricate well of the well (WOW) dishes with arbitrary 3D microwell shapes for improved embryo culture

Despite its high cost, the success rate for in vitro fertilization (IVF) remains < 33% in humans, driving the need for new techniques to improve embryo culture outcomes. The well-of-the-well (WOW) culture system is a platform for in-vitro mammalian embryo culture that has been shown to enhance the developmental competence of embryos and clinical pregnancy rates in humans. However, discovery and testing of the best design for optimal embryo culture quality is hindered by the lack of a method to flexibly produce WOW dishes of various designs. Here, we present a low-cost and simple method to fabricate WOW dishes with microwells of arbitrary shapes and dimensions. We use a low-cost 3D printing service to fabricate a poly(dimethylsiloxane) (PDMS)-based WOW insert that can be paired with a standard in vitro fertilization (IVF) dish to create WOW dishes with new microwell shapes, including pyramidal and hemispherical designs. We validate the fabrication quality of the WOW inserts and demonstrate the utility of the assembled WOW dishes for observation and grading of mouse embryo quality. Moreover, our results indicate that WOW dishes with hemispherical microwells result in better culture outcomes than traditional flat-bottomed IVF dishes and those with other microwell shapes, including the semi-elliptical microwells used in commercial WOW dishes. The proposed fabrication strategy thus provides a way to rapidly fabricate and test new WOW dishes that may bolster IVF success rates.

Soft Kirigami Composites for Form‐Finding of Fully Flexible Deployables

AbstractA new class of thin flexible structures is introduced that morph from flat into prescribed 3D shapes through strain mismatch between layers of a composite plate. To achieve control over the target shape, two different concepts are coupled. First, motivated by biological growth, strain mismatch is applied between the flat composite layers to transform it into a 3D shape. Depending on the amount of the applied strain mismatch, the transformation involves buckling into one of the available finite number of deformation modes. Second, inspired by kirigami, portions of the material are removed from one of the layers according to a specific pattern. This dramatically increases the number of possible 3D shapes and allows us to attain specific topologies. An experimental apparatus that allows precise control of the strain mismatch is devised. An inverse problem is posed, where starting from a given target shape, the physical parameters that make these shapes possible are determined. To show how the concept works, it focuses on circular composite plates and designs a kirigami pattern that yields a hemispherical structure. The analysis combines a theoretical approach with numerical simulations and physical experiments to understand and predict the shape transition from 2D to 3D. The tools developed here can be extended to attain arbitrary 3D shapes. The initially flat shape suggests that conventional additive manufacturing techniques can be used to functionalize the soft kirigami composite to fabricate, for example, deployable 3D structures, smart skins, and soft electromagnetic metasurfaces.

A Comprehensive Review of DNA Origami Stabilization Techniques

In recent years, DNA has emerged as a powerful tool in the field of nanotechnology. The DNA origami technique is largely responsible for this, revolutionizing nanofabrication due to its controllability, precision, and ability to leverage DNA’s unique properties. The technique consists of folding a long, single-stranded DNA (called a scaffold strand) by binding it with shorter staple strands to create almost any shape desired. With a desired structure in mind, researchers can design and assemble scaffold and staple strands using computer software like Cadnano or Tiamat. This is possible because of the Watson-Crick base pairing of DNA strands, which allows for programmable self-assembly of DNA nanostructures and therefore, the synthesis of arbitrary 2D and 3D shapes. Because DNA is a biomolecule,the nanostructures are also biocompatible and can be employed in biological applications including drug delivery. DNA origami nanostructures are not only limited to biological applications; they have also found uses in nanophotonics, plasmonics, and electronics. However, DNA origami still faces many challenges before it can be widely adopted. One such challenge is ensuring stability, and thus guaranteeing the performance of the DNA origami, in the presence of heat, nuclease in organic bodies, and chaotropic agents. This warrants the question: what methodologies can be employed to best stabilize DNA origami structures? This paper further focuses on two methods: covalently binding various molecules by cross-linking and non-binding encapsulation. Detailed analysis and comparison between various molecules used to bind and coat DNA nanostructures is used to evaluate performance and applicability of each method. In the end an oligolysines coating cross-linked with glutaraldehyde was found to have the strongest biological stability, thymine cross-linking had the strongest thermal stability, a silica coating had the best stability against the largest number of factors, and both graphene and Al3O2 coatings had the best mechanical stability.

Regenerative Living 4D Printing via Reversible Growth of Polymer Networks.

Living creatures possess complex geometries, exceptional adaptability, and continuous growing and regenerating characteristics, which are difficult for synthetic materials to imitate simultaneously. A living polymer network with these features is reported. The polymer can be digitally printed into arbitrary 3D shapes and subsequently undergoes growth via living polymerization of a monomer as the nutrient. This leads to macroscopic dimensional growth and transforms the printed amorphous network into a crystallizable network, resulting in geometric adaptability via a shape-memory mechanism. By controlling the localized growth, an initial homogeneous structure can be converted into a geometrically different heterogeneous structure composed of materials with different properties (crystallization and mechanical properties). After growth, the original network can be chemically regenerated for regrowth. With this regenerative living 4D printing, one 3D-printed seed template can be turned into different derivatives with distinct geometries and mechanical properties when repeated regeneration is conducted in different localized regions and the degree of regrowth is varied.

Assemble Them All

Assembly planning is the core of automating product assembly, maintenance, and recycling for modern industrial manufacturing. Despite its importance and long history of research, planning for mechanical assemblies when given the final assembled state remains a challenging problem. This is due to the complexity of dealing with arbitrary 3D shapes and the highly constrained motion required for real-world assemblies. In this work, we propose a novel method to efficiently plan physically plausible assembly motion and sequences for real-world assemblies. Our method leverages the assembly-by-disassembly principle and physics-based simulation to efficiently explore a reduced search space. To evaluate the generality of our method, we define a large-scale dataset consisting of thousands of physically valid industrial assemblies with a variety of assembly motions required. Our experiments on this new benchmark demonstrate we achieve a state-of-the-art success rate and the highest computational efficiency compared to other baseline algorithms. Our method also generalizes to rotational assemblies (e.g., screws and puzzles) and solves 80-part assemblies within several minutes.

Three-Dimensional Formation Control for Robot Swarms

This article addresses a distributed 3D algorithm for coordinating a swarm of autonomous robots (ARs) to spatially self-aggregate into an arbitrary shape based on only local interactions. Each AR has local proximity sensors for measuring the relative coordinates of its nearby AR. We assume that only a single AR, called the leader, can localize itself in global coordinate systems, while accessing the arbitrary user-specified 3D shape. We further assume that the leader has a communication ability superior to all other ARs so that the leader can directly send a communication signal to any other AR inside the shape. Our aim is to make the ARs maneuver while maintaining a user-specified 3D formation such that the network connection of all ARs is maintained during the maneuver. Our approach results in a 3D formation shape and does not need the global localization of an AR, except for the leader. To the best of our knowledge, our study is novel in the construction of a 3D formation for covering an arbitrary shape such that network connection is maintained while ARs maneuver based on local communication. We further show that the proposed control yields reliable accuracy against significant AR failures and movement error. Utilizing MATLAB simulations, we demonstrate the outperformance of the proposed formation controls.

P‐82: Prediction Methodology for the Optical Properties of QDs with Arbitrary 3D Shape

Recently, researchers have strived to enhance the optical characteristics of Quantum Dots (QDs) by carefully tailoring the QD material composition, shape and size. In this endeavor, simulation proves an invaluable tool to assess and optimize the QD structure. In this paper, the optical properties of QD structures are explored, taking into account the complex band structure of the QD and its geometric structure, as well as the probability of quantum state transitions. In order to perform this analysis the In‐house Simulator (DaVinci_QD), based on 3D FEM, was developed and applied. We demonstrate that the DaVinci_QD simulator exhibits good agreement with experiment, providing confidence that the tool be applied to predict optical characteristics with accuracy. The simulator is applied to state‐of�the‐art QDs that are currently under development, offering additional insight to aid in the analysis of optical measurements and revealing paths to further performance enhancement. In this way, simulation can replace experiment and QD structures can be optimized rapidly.

A theoretical scheme for shape-programming of thin hyperelastic plates through differential growth

In this paper, a theoretical scheme is proposed for shape-programming of thin hyperelastic plates through differential growth. First, starting from the 3D governing system of a hyperelastic (neo-Hookean) plate, a consistent finite-strain plate equation system is formulated through a series expansion and truncation approach. Based on the plate equation system, the problem of shape-programming is studied under the stress-free assumption. By equating the stress components in the plate equations to be zero, the explicit relations between the growth functions and the geometrical quantities of target shape of the plate are derived. Then, a theoretical scheme of shape-programming is proposed, which can be used to identify the growth fields corresponding to arbitrary 3D shapes of the plate. To demonstrate the efficiency of the scheme, some typical examples are studied. The predicted growth functions in these examples are adopted in the numerical simulations, from which the target shapes of the plate can be recovered completely. The scheme of shape-programming proposed in the current work is applicable for manufacturing intelligent soft devices.

One-Pot Synthesis of Melt-Processable Supramolecular Soft Actuators.

The application of reprocessable and reprogrammable soft actuators is limited by the synthetic strategies, 3D‐shaping capabilities, and small deformations. In this work, melt‐processable supramolecular soft actuators based on segmented copolymers containing thiourethane and liquid crystal segments have been prepared via sequential thiol addition reactions in a one‐pot approach using commercially available building blocks. The actuators demonstrated immediate, reversible response and weightlifting capabilities with large deformations up to 32 %. Through exploiting the supramolecular cross‐links, the material could be recycled and reprogrammed into 3D actuators and welded into an actuator assembly with different deformation modes. Our work offers a one‐pot synthesis and straightforward melt‐processable approach to prepare supramolecular soft actuators with large deformations that can be reprocessed and reprogrammed into arbitrary 3D shapes.

One‐Pot Synthesis of Melt‐Processable Supramolecular Soft Actuators

AbstractThe application of reprocessable and reprogrammable soft actuators is limited by the synthetic strategies, 3D‐shaping capabilities, and small deformations. In this work, melt‐processable supramolecular soft actuators based on segmented copolymers containing thiourethane and liquid crystal segments have been prepared via sequential thiol addition reactions in a one‐pot approach using commercially available building blocks. The actuators demonstrated immediate, reversible response and weightlifting capabilities with large deformations up to 32 %. Through exploiting the supramolecular cross‐links, the material could be recycled and reprogrammed into 3D actuators and welded into an actuator assembly with different deformation modes. Our work offers a one‐pot synthesis and straightforward melt‐processable approach to prepare supramolecular soft actuators with large deformations that can be reprocessed and reprogrammed into arbitrary 3D shapes.

Design and additive manufacturing of thermal metamaterial with high thermal resistance and cooling capability

Abstract Metamaterials are defined as artificially designed micro-architectures with unusual physical properties, including optical, electromagnetic, mechanical, and thermal properties. This study proposes a thermal metamaterial that provides an efficient thermal cycle with two conflicting objectives: (i) high thermal resistance as a thermal insulator and (ii) high cooling capability as a heat exchanger. To enable these conflicting objectives, we used cellular lattice structures fabricated by additive manufacturing (AM). An efficient design method based on a finite element (FE) mesh was developed to obtain boundary-conformal lattices for arbitrary 3D shapes. FE analyses were then conducted to evaluate the structural and thermal behaviors of the lattice structures. The designed lattice structures were fabricated by powder-bed fusion (PBF) type AM using Ti-6Al-4V powders. Heat conduction tests were then performed to evaluate the thermal resistance of the lattices with various strut diameters, and the resulting thermal resistance increased five to fifteen times in comparison with that of the pure material. Cooling tests were also conducted to evaluate the cooling capability of the lattices, which showed that the lattice structures could act not only as a thermal insulator but also as a heat exchanger. Consequently, the developed lattice structures can be regarded as a thermal metamaterial that is useful in various applications that require a high thermal cycle of heating and cooling.

Automated Geometric Registration for Multi-Projector Displays on Arbitrary 3D Shapes Using Uncalibrated Devices.

In this article we present a completely automated and scalable multi-projector registration system that allows multiple uncalibrated projectors and cameras on arbitrary shape surfaces. Our method estimates the parameters of multiple uncalibrated tiled or superimposed projectors, the extrinsic parameters of the observing cameras (with known intrinsic parameters), the shape of the illuminated 3D geometry and geometrically registers the projectors on it. This is achieved without using any fiducials, even if part of the surface is visible to only one camera. The method uses a completely automatic approach for cross-correlation and cross-validation of the device parameters and the surface geometry resulting in an accurate registration on the arbitrary unknown geometry that does not need an accurate prior calibration of each of the uncalibrated devices using physical patterns or fiducials. Estimating projector parameters allows for quick recalibration of the system in the face of projector movements, by re-estimating only the parameters of the moved projector and not the entire system. Thus, our work can enable easy deployment of spatially augmented reality environments of different sizes (from small table top objects to large immersive environments), different shapes (inside-looking-out or outside-looking in), and different configurations (tiled or superimposed) using the same proposed method.

2D material programming for 3D shaping

Two-dimensional (2D) growth-induced 3D shaping enables shape-morphing materials for diverse applications. However, quantitative design of 2D growth for arbitrary 3D shapes remains challenging. Here we show a 2D material programming approach for 3D shaping, which prints hydrogel sheets encoded with spatially controlled in-plane growth (contraction) and transforms them to programmed 3D structures. We design 2D growth for target 3D shapes via conformal flattening. We introduce the concept of cone singularities to increase the accessible space of 3D shapes. For active shape selection, we encode shape-guiding modules in growth that direct shape morphing toward target shapes among isometric configurations. Our flexible 2D printing process enables the formation of multimaterial 3D structures. We demonstrate the ability to create 3D structures with a variety of morphologies, including automobiles, batoid fish, and real human face.

Next Best View Planning via Reinforcement Learning for Scanning of Arbitrary 3D Shapes

Reconstructing 3D objects from scanned measurements is a fundamental task in computer vision. A central factor for the effectiveness of 3D reconstruction is the selection of sensor views for scanning. The latter remains an open problem in the 3D geometry processing area, known as the next-best-view planning problem, and is commonly approached by combinatorial or greedy methods. In this work, we propose a reinforcement learning-based approach to sequential next-best-view planning. The method is implemented based on the gym environment including 3D reconstruction, next-best-scan planning, and image acquisition features. We demonstrate this method to outperform the baselines in terms of the number of required scans and the obtained 3D mesh reconstruction accuracy.

A unified methodology for calculation of compliance and stiffness contribution tensors of inhomogeneities of arbitrary 2D and 3D shapes embedded in isotropic matrix – open access software.

Property contribution tensors constitute basic building blocks for evaluation of effective properties of heterogeneous materials. Most of the existing results, however, are obtained for inhomogeneities of simple shapes, like ellipsoidal. With this paper we introduce open access software that allows one to calculate the components of compliance and stiffness contribution tensors of inhomogeneities of any shape. The software uses mesh free method based on a class of Gaussian approximating functions. Here we present details of the method and illustrate it by several examples. The software can be downloaded from the website of NMSU Center for Micromechanics https://centerformichromechanics.nmsu.edu.

TilinGNN

We introduce the first neural optimization framework to solve a classical instance of the tiling problem. Namely, we seek a non-periodic tiling of an arbitrary 2D shape using one or more types of tiles---the tiles maximally fill the shape's interior without overlaps or holes. To start, we reformulate tiling as a graph problem by modeling candidate tile locations in the target shape as graph nodes and connectivity between tile locations as edges. Further, we build a graph convolutional neural network , coined TilinGNN, to progressively propagate and aggregate features over graph edges and predict tile placements. TilinGNN is trained by maximizing the tiling coverage on target shapes, while avoiding overlaps and holes between the tiles. Importantly, our network is self-supervised , as we articulate these criteria as loss terms defined on the network outputs, without the need of ground-truth tiling solutions. After training, the runtime of TilinGNN is roughly linear to the number of candidate tile locations, significantly outperforming traditional combinatorial search. We conducted various experiments on a variety of shapes to showcase the speed and versatility of TilinGNN. We also present comparisons to alternative methods and manual solutions, robustness analysis, and ablation studies to demonstrate the quality of our approach.

A supervised neural network for drag prediction of arbitrary 2D shapes in laminar flows at low Reynolds number

Despite the significant breakthrough of neural networks in the last few years, their spreading in the field of computational fluid dynamics is very recent, and many applications remain to explore. In this paper, we explore the drag prediction capabilities of convolutional neural networks for laminar, low-Reynolds number flows past arbitrary 2D shapes. A set of random shapes exhibiting a rich variety of geometrical features is built using Bézier curves. The efficient labelling of the shapes is provided using an immersed method to solve a unified Eulerian formulation of the Navier-Stokes equation. The network is then trained and optimized on the obtained dataset, and its predictive efficiency assessed on several real-life shapes, including NACA airfoils.

Region-based Fitting of Overlapping Ellipses and its application to cells segmentation

We present RFOVE, a region-based method for approximating an arbitrary 2D shape with an automatically determined number of possibly overlapping ellipses. RFOVE is completely unsupervised, operates without any assumption or prior knowledge on the object's shape and extends and improves the Decremental Ellipse Fitting Algorithm (DEFA) [1]. Both RFOVE and DEFA solve the multi-ellipse fitting problem by performing model selection that is guided by the minimization of the Akaike Information Criterion on a suitably defined shape complexity measure. However, in contrast to DEFA, RFOVE minimizes an objective function that allows for ellipses with higher degree of overlap and, thus, achieves better ellipse-based shape approximation. A comparative evaluation of RFOVE with DEFA on several standard datasets shows that RFOVE achieves better shape coverage with simpler models (less ellipses). As a practical exploitation of RFOVE, we present its application to the problem of detecting and segmenting potentially overlapping cells in fluorescence microscopy images. Quantitative results obtained in three public datasets (one synthetic and two with more than 4000 actual stained cells) show the superiority of RFOVE over the state of the art in overlapping cells segmentation.

Compacting Voxelized Polyhedra via Tree Stacking

AbstractVolume compaction is a geometric problem that aims to reduce the volume of a polyhedron via shape transform. Compactable structures are easier to transport and in some cases easier to manufacture, therefore, they are commonly found in our daily life (e.g. collapsible containers) and advanced technology industries (e.g., the recent launch of 60 Starlink satellites compacted in a single rocket by SpaceX). It is known in the literature that finding a universal solution to compact an arbitrary 3D shape is computationally challenging. Previous approaches showed that stripifying mesh surface can lead to optimal compaction, but the resulting structures were often impractical. In this paper, we propose an algorithm that cuts the 3D orthogonal polyhedron, tessellated by thick square panels, into a tree structure that can be transformed into compact piles by folding and stacking. We call this process tree stacking. Our research found that it is possible to decompose the problem into a pipeline of several solvable local optimizations. We also provide an efficient algorithm to check if the solution exists by avoiding the computational bottleneck of the pipeline. Our results show that tree stacking can efficiently generate stackable structures that have better folding accuracy and similar compactness comparing to the most compact stacking using strips.

Freestanding laser induced graphene paper based liquid sensors

The recently emergent laser induced graphene paper (LIGP) has endowed the large-scale assembly of 2D electronics with freestanding and multifunctional natures. Liquid sensors, a crucial component in next-generation smart devices, were developed in this report through LIGP technology. Attributed to the one-step process of computer-aided laser scribing, the new sensor was fulfilled easily with arbitrary 2D shapes, sizes and array modalities. In comparison to indirect sensing manners existed majorly in polymer binder/substrate enabled carbon devices, the novel LIGP sensors with self-supported graphitic structures evidently showed multiple superiorities, including faster response, greater reproducibility, and higher sensitivity for micro-volume liquid detections. Benefiting from the laser-process dependent structures, we further proved the tunable sensing characteristics relying dominantly on specific surface area, a critical factor for introducing capillary pressure to facilitate liquid infiltration and electrical disruption. Additionally, a dual-mode liquid-recognition strategy based on both time and speed was creatively proposed for identification of various liquids. With successful utilization of 5 × 5 LIGP array for mapping the distribution of different fluid emissions on composite structures, we highly anticipate the escalation of LIGP sensors for commercialization, roll-to-roll manufacturing, and multimodal safety-assurance applications.