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To improve the performance of the truss robot and guide the subsequent design of the telescopic arm of the truss robot under different working conditions, the working principle of the telescopic arm of the truss robot is introduced, and a mathematical model for the size optimization of the telescopic arm is established under a given working condition with the goal of minimizing the mass of the telescopic arm and ensuring that the telescopic arm has a certain stiffness. According to the optimized dimension parameters, the prototype of the telescopic arm of the platform truss robot is manufactured and run.

Inspired by crystallography, the periodic assembly of trusses into architected materials has enjoyed popularity for more than a decade and produced countless cellular structures with beneficial mechanical properties. Despite the successful and steady enrichment of the truss design space, the inverse design has remained a challenge: While predicting effective truss properties is now commonplace, efficiently identifying architectures that have homogeneous or spatially varying target properties has remained a roadblock to applications from lightweight structures to biomimetic implants. To overcome this gap, we propose a deep-learning framework, which combines neural networks with enforced physical constraints, to predict truss architectures with fully tailored anisotropic stiffness. Trained on millions of unit cells, it covers an enormous design space of topologically distinct truss lattices and accurately identifies architectures matching previously unseen stiffness responses. We demonstrate the application to patient-specific bone implants matching clinical stiffness data, and we discuss the extension to spatially graded cellular structures with locally optimal properties.

The rack plate is the core structural part for conventional truss-type legs, meanwhile the rack plate limits the economical efficiency of legs due to its characteristics. Aiming at the limit of rack plate, this paper raises a type of pin-type chord for a truss-type leg according to engineering experience. Based on the same weight, taking a self-elevated platform for example, this study uses FEA software to analyze and compare the loads and leg results of rack-type chords and pin-type chords. The result shows the pin-type chord has better mechanical properties, which provides a reference for the optimization of the subsequent self-elevated truss-type leg.

It has been a long-standing challenge in modern material design to create low-density, lightweight materials that are simultaneously robust against defects and can withstand extreme thermomechanical environments, as these properties are often mutually exclusive: The lower the density, the weaker and more fragile the material. Here, we develop a process to create nanoarchitected carbon that can attain specific strength (strength-to-density ratio) up to one to three orders of magnitude above that of existing micro- and nanoarchitected materials. We use two-photon lithography followed by pyrolysis in a vacuum at 900 °C to fabricate pyrolytic carbon in two topologies, octet- and iso-truss, with unit-cell dimensions of ∼2 μm, beam diameters between 261 nm and 679 nm, and densities of 0.24 to 1.0 g/cm³. Experiments and simulations demonstrate that for densities higher than 0.95 g/cm³ the nanolattices become insensitive to fabrication-induced defects, allowing them to attain nearly theoretical strength of the constituent material. The combination of high specific strength, low density, and extensive deformability before failure lends such nanoarchitected carbon to being a particularly promising candidate for applications under harsh thermomechanical environments.

This paper illustrates experimentally the significance of using hybrid steel truss reinforced concrete beam (HSTRCB) in the construction of aerospace shelters. A finite element analysis was carried out in a previous stage to compare the flexural behaviour of HSTRCB with steel truss beams (without concrete casting) and traditional reinforced concrete beams. The Finite element simulations showed a significant improvement in the flexural behaviour of the HSTRCB. In this paper two beams were tested by 4-point bending test. The first is standard reinforced concrete beam. The other is HSTRCB with steel angles as top and bottom reinforcement. Stirrups were replaced by vertical steel plates. Flexural behaviour was represented by the load-deflection curves. Results showed that the adhesion strength between steel truss members and concrete should be enhanced. Failure occurred due to slippage between steel truss elements and concrete. Moreover, HSTRCB showed a noticeable improvement in the ductile behaviour for the zone after the ultimate capacity.

The rise of machine learning has fueled the discovery of new materials and, especially, metamaterials—truss lattices being their most prominent class. While their tailorable properties have been explored extensively, the design of truss-based metamaterials has remained highly limited and often heuristic, due to the vast, discrete design space and the lack of a comprehensive parameterization. We here present a graph-based deep learning generative framework, which combines a variational autoencoder and a property predictor, to construct a reduced, continuous latent representation covering an enormous range of trusses. This unified latent space allows for the fast generation of new designs through simple operations (e.g., traversing the latent space or interpolating between structures). We further demonstrate an optimization framework for the inverse design of trusses with customized mechanical properties in both the linear and nonlinear regimes, including designs exhibiting exceptionally stiff, auxetic, pentamode-like, and tailored nonlinear behaviors. This generative model can predict manufacturable (and counter-intuitive) designs with extreme target properties beyond the training domain. Truss metamaterials are ubiquitous but their vast design space is far from fully explored. Here, authors use machine learning to present a unified, continuous latent space description, enabling the rapid generation of trusses with tunable or exceptional linear and nonlinear mechanical properties.

Two-photon polymerization (TPP) is the most precise 3-D printing process that has been used to create many complex structures for advanced photonic and nanoscale applications. However, to date the technology still remains a laboratory tool due to its high operation cost and limited fabrication rate, i.e., serial laser scanning process. Here we present a revolutionary laser nanofabrication process based on TPP and an ultrafast random-access digital micromirror device (DMD) scanner. By exploiting binary holography, the DMD scanner can simultaneously generate and individually control one to tens of laser foci for parallel nanofabrication at 22.7 kHz. Complex 3-D trusses and woodpile structures have been fabricated via single or multi-focus processes, showing a resolution of ~500 nm. The nanofabrication system may be used for largescale nano-prototyping or creation of complex structures, e.g., overhanging structures, that cannot be easily fabricated via conventional raster-scanning-based systems, bringing significant impact to the world of nanomanufacturing. Two photon polymerization (TPP) allows nanofabrication of three dimensional objects with complex geometries, but is considered to be slow with a limited fabrication rate. Here the authors present a TPP technique based on a digital mirror device scanner which allows for fast parallel nanofabrication with improved precision and flexibility.

Since its introduction, the ground structure method has been used in the derivation of closed–form analytical solutions for optimal structures, as well as providing information on the optimal load–paths. Despite its long history, the method has seen little use in three–dimensional problems or in problems with non–orthogonal domains, mainly due to computational implementation difficulties. This work presents a methodology for ground structure based topology optimization in arbitrary three–dimensional (3D) domains. The proposed approach is able to address concave domains and with the possibility of holes. In addition, an easy–to–use implementation of the proposed algorithm for the optimization of least–weight trusses is described in detail. The method is verified against three–dimensional closed–form solutions available in the literature. By means of examples, various features of the 3D ground structure approach are assessed, including the ability of the method to provide solutions with different levels of detail. The source code for a MATLAB implementation of the method, named GRAND3 — GRound structure ANalysis and Design in 3D , is available in the (electronic) Supplementary Material accompanying this publication.

The present work describes in detail an implementation of the ground structure method for non–orthogonal unstructured and concave domains written in MATLAB, called GRAND — GRound structure ANalysis and Design . The actual computational implementation is provided, and example problems are given for educational and testing purposes. The problem of ground structure generation is translated into a linear algebra approach, which is inspired by the video–game literature . To prevent the ground structure generation algorithm from creating members within geometric entities that no member should intersect (e.g. holes, passive regions), the concept of “restriction zones” is employed, which is based on collision detection algorithms used in computational geometry and video–games . The aim of the work is to provide an easy–to–use implementation for the optimization of least–weight trusses embedded in any domain geometry.

Solanki discusses an article by Indu Geevar and Devdas Menon on the strength of reinforced concrete pier caps to validate the strut-and-tie method. The authors showed an eccentricity in a loading frame but did not show the amount of eccentricity, because the eccentricity of the loading will develop moment and it has to be considered in the truss model, either in a compressive truss or in a tension tie. Meanwhle, Geevar and Menon respond to Solanki's comments on their article.