STR-854: MULTIFUNCTIONAL AND MULTIPHYSICS MATERIALS AS LOAD-BEARING STRUCTURAL COMPONENTS

Multifunctional and multiphysics cellular solids are introduced in this paper as load-bearing structural components. Cellular solids offer a robust low-mass alternative for applications requiring lightweight and stiff components. The unique properties of cellular solids are achieved through cell geometry, connectivity, relative density, and properties of constituent materials. Inspired by biological systems, smart cellular solids can integrate low-mass, sensing/actuating, and self-healing properties into structural components. Discrete fabrication, by integrating patches of smart/active materials onto cellular solids, and continuous fabrication, using additive manufacturing, are two fabrication techniques for the manufacturing of multifunctional cellular solids. We propose a multiscale methodology for the analysis and design of smart cellular structures on the basis of homogenization, structural hierarchy, multiphysics simulation, and multi-objective optimization. It is shown that relative density, cell microarchitechture, cell topology, and volume fraction play a considerable role on the characteristics of multifunctional materials. At last, the potential application of smart cellular solids in civil and building construction industry are reviewed. The paper sheds lights on the emergence of multifunctional and multiphysics materials in industrial sectors and introduces the effect of tailoring the architecture of smart cellular solids in multiple scales on tuning and optimizing the structural functionality

Accelerated design of architectured ceramics with tunable thermal resistance via a hybrid machine learning and finite element approach

Abstract Topologically interlocked architectures can transform brittle ceramics into tougher materials, while making the material design procedure a cumbersome task since modeling the whole architectural design space is not efficient and, to a degree, is not viable. We propose an approach to design architectured ceramics using machine learning (ML), trained by finite element analysis data and together with a self-learning algorithm, to discover high-performance architectured ceramics in thermomechanical environments. First, topologically interlocked panels are parametrically generated. Then, a limited number of designed architectured ceramics subjected to a thermal load is studied. Finally, the multilinear perceptron is employed to train the ML model in order to predict the thermomechanical performance of architectured panels with varied interlocking angles and number of blocks. The developed feed-forward artificial neural network framework can boost the architectured ceramic design efficiency and open up new avenues for controllability of the functionality for various high-temperature applications. This study demonstrates that the architectured ceramic panels with the ML-assisted engineered patterns show improvement up to 30% in frictional energy dissipation and 7% in the sliding distance of the tiles and 80% reduction in the strain energy, leading to a higher safety factor and the structural failure delay compared to the plain ceramics

Fused filament fabrication of PVDF films for piezoelectric sensing and energy harvesting applications

Fused filament fabrication (FFF) of piezoelectric polymer polyvinylidene fluoride (PVDF) provides a simple manufacturing technique for the fabrication of lead-free piezoelectric devices compared to the traditional manufacturing methods, such as large-scale film extrusion and solution casting. Here, we investigate the effects of the stretching and poling parameters on the enhancement of piezoelectric performance of the printed PVDF films. The stretched and polarized PVDF films with dimensions of 40 × 20 × 0.06 mm (length × width × thickness) possess a piezoelectric charge coefficient (d33) of 7.29 pC N−1 and a fraction of β phase (Fβ) of 65% at a stretching ratio (R) of 4 after being polarized under an electric field of 30 V μm−1. The resulting d33 of the fabricated PVDF films has been substantially enhanced by ∼10–100 times higher than the related reported values of the FFF printed PVDF films. The fabricated PVDF films are capable of detecting compression (d33) and vibration (d31). By blowing four piezoelectric films connected in parallel for 3 min, the energy stored in the capacitor can make a LED blink. Our fabricated piezoelectric PVDF films could be used in the field of pressure sensing, vibration sensing and energy harvesting applications

Evaluation and Importance-Performance Analysis of LARG Supply Chain Practices in Dairy Industries (Case Study: Kalleh Dairy Company)

Today, one of the main concerns of managers interested in novel paradigms of supply chain (SC) is identification of improvement priorities and determination of the focal points to promote the performance of SC. Studying the current performance situation of effective practices in the three tiers of Kalleh SC (first tier supplier, focal firm and first tier distributor), the present paper seeks to identify key components of LARG (Lean, Agile, Resilient and Green) SC and improve them in the company under study by developing a practical and strategic decision making model. The results indicate that the greatest gap in Pole Co. lies in just-in-time production measures and the smallest gap in the ability to reduce likely risks. In Kalleh Co., the greatest gap was found in close & long term relationship with suppliers and the least one was in the development of visibilities in SC. The greatest gap in Banchow was quick responsiveness to customers and the least one was reduction of likely risks

Dragonfly‐Inspired Wing Design Enabled by Machine Learning and Maxwell's Reciprocal Diagrams

Abstract This research is taking the first steps toward applying a 2D dragonfly wing skeleton in the design of an airplane wing using artificial intelligence. The work relates the 2D morphology of the structural network of dragonfly veins to a secondary graph that is topologically dual and geometrically perpendicular to the initial network. This secondary network is referred as the reciprocal diagram proposed by Maxwell that can represent the static equilibrium of forces in the initial graph. Surprisingly, the secondary graph shows a direct relationship between the thickness of the structural members of a dragonfly wing and their in‐plane static equilibrium of forces that gives the location of the primary and secondary veins in the network. The initial and the reciprocal graph of the wing are used to train an integrated and comprehensive machine‐learning model that can generate similar graphs with both primary and secondary veins for a given boundary geometry. The result shows that the proposed algorithm can generate similar vein networks for an arbitrary boundary geometry with no prior topological information or the primary veins' location. The structural performance of the dragonfly wing in nature also motivated the authors to test this research's real‐world application for designing the cellular structures for the core of airplane wings as cantilever porous beams. The boundary geometry of various airplane wings is used as an input for the design proccedure. The internal structure is generated using the training model of the dragonfly veins and their reciprocal graphs. One application of this method is experimentally and numerically examined for designing the cellular core, 3D printed by fused deposition modeling, of the airfoil wing; the results suggest up to 25% improvements in the out‐of‐plane stiffness. The findings demonstrate that the proposed machine‐learning‐assisted approach can facilitate the generation of multiscale architectural patterns inspired by nature to form lightweight load‐bearable elements with superior structural properties

Snapping Mechanical Metamaterials under Tension

A snapping mechanical metamaterial is designed, which exhibits a sequential snap‐through behavior under tension. The tensile response of this mechanical metamaterial can be altered by tuning the architecture of the snapping segments to achieve a range of nonlinear mechanical responses, including monotonic, S‐shaped, plateau, and non‐monotonic snap‐through behavior

Lessons from Nature for Carbon‐Based Nanoarchitected Metamaterials

Bioinspired materials often achieve superior mechanical properties owing to their microscale architectures that resemble design motifs in biological materials. The bioinspired architectures can be extended to nanoscale, where carbon‐based materials, including graphene and carbon nanotubes, are excellent candidates as building blocks. This study introduces carbon‐based nanoarchitected metamaterials inspired by seven biological design motifs, i.e., cellular, gradient, tubular, fibrous, helicoidal, suture, and layered structures. Numerical studies based on molecular dynamics simulation along with continuum‐based finite element analysis are conducted for each bioinspired design to examine the unique mechanical properties, namely specific stiffness, specific strength, failure strain, and specific energy absorption, under tensile/shear loading conditions. Different deformation and failure mechanisms found by molecular simulation and continuum mechanics are discussed. The numerical results show that the mechanical properties of the introduced bioinspired and carbon‐based nanoscale designs may surpass the performance of the conventional carbon‐based counterparts. The developed nanoarchitected metamaterials demonstrate instances of possibilities for filling the empty regions in the Ashby charts to attain lightweight advanced materials that can also break the trade‐off between strength and failure strain. These findings impart lessons from the constitutive structure of biological materials to form the next generation of multifunctional architected metamaterials with rationally designed nano‐architectures