A mechanical reduced order model for elastomeric 3D printed architectures

Functionally graded materials (FGMs) and functionally graded structures (FGSs) are special types of advanced composites with peculiar features and advantages. This article reviews the design criteria of functionally graded additive manufacturing (FGAM), which is capable of fabricating gradient components with versatile functional properties. Conventional geometrical‐based design concepts have limited potential for FGAM and multi‐scale design concepts (from geometrical patterning to microstructural design) are needed to develop gradient components with specific graded properties at different locations. FGMs and FGSs are of great interest to a larger range of industrial sectors and applications including aerospace, automotive, biomedical implants, optoelectronic devices, energy absorbing structures, geological models, and heat exchangers. This review presents an overview of various fabrication ideas and suggestions for future research in terms of design and creation of FGMs and FGSs, benefiting a wide variety of scientific fields.

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“…In addition, these methods rarely use TO to optimize complex models except for some simple cubic architectures. [47,53]…”

Section: Topology Optimizationmentioning

confidence: 99%

A Review on Functionally Graded Materials and Structures via Additive Manufacturing: From Multi‐Scale Design to Versatile Functional Properties

Yan

Feng

Liu

et al. 2020

Adv Materials Technologies

304

116

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“…Later, Maskery et al [82] investigated the effect of cell topology, orientation, and volume fraction to provide a specified stiffness in the lattice structures. Further, Weisgraber et al [83] conducted research works where they proved that by modifying cell parameters, it is possible to control the stress-strain curve of a Cellular Material to assess specific load requirements and performance. A similar study was developed by Wang et al [84], where the authors optimized lattice structures to provide specific stiffness for given multi-loading scenarios in the low volume fraction limit.…”

Section: Tailoring Stiffness Via Cellular Materialsmentioning

confidence: 99%

A Review on Tailoring Stiffness in Compliant Systems, via Removing Material: Cellular Materials and Topology Optimization

2021

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Cellular Materials and Topology Optimization use a structured distribution of material to achieve specific mechanical properties. The controlled distribution of material often leads to several advantages including the customization of the resulting mechanical properties; this can be achieved following these two approaches. In this work, a review of these two as approaches used with compliance purposes applied at flexure level is presented. The related literature is assessed with the aim of clarifying how they can be used in tailoring stiffness of flexure elements. Basic concepts needed to understand the fundamental process of each approach are presented. Further, tailoring stiffness is described as an evolutionary process used in compliance applications. Additionally, works that used these approaches to tailor stiffness of flexure elements are described and categorized. Finally, concluding remarks and recommendations to further extend the study of these two approaches in tailoring the stiffness of flexure elements are discussed.

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“…Although considerable progress has been achieved in hyperelastic finite deformation theory, the equation given by finite elasticity theory is complex and challenging to solve. [25] The abstraction and simplification of the related structural parameters inevitably cause a reduction in the actual performance space, which offsets the range of possible performance that can be achieved by realizing a large number of structures utilizing digital preparation technology. [26] Expensive data collection costs still impede DL as a powerful tool for constructing constitutive relation for additive manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Studying Complex Evolution of Hyperelastic Materials under External Field Stimuli using Artificial Neural Networks with Spatiotemporal Features in a Small‐Scale Dataset

Chai²,

Xiong

et al. 2022

Advanced Materials

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Deep‐learning (DL) methods, in consideration of their excellence in dealing with highly complex structure–performance relationships for materials, are expected to become a new design paradigm for breakthroughs in material performance. However, in most cases, it is impractical to collect massive‐scale experimental data or open‐source theoretical databases to support training DL models with sufficient prediction accuracy. In a dataset consisting of 483 porous silicone rubber observations generated via ink‐writing additive manufacturing, this work demonstrates that constructing low‐dimensional, accurate descriptors is the prerequisite for obtaining high‐precision DL models based on small experimental datasets. On this basis, a unique convolutional bidirectional long short‐term memory model with spatiotemporal features extraction capability is designed, whose hierarchical learning mechanism further reduces the requirement for the amount of data by taking full advantage of data information. The proposed approach can be expected as a powerful tool for innovative material design on small experimental datasets, which can also be used to explore the evolutionary mechanisms of the structures and properties of materials under complex working conditions.

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A mechanical reduced order model for elastomeric 3D printed architectures

Abstract: Abstract

Cited by 12 publications

References 20 publications

A Review on Functionally Graded Materials and Structures via Additive Manufacturing: From Multi‐Scale Design to Versatile Functional Properties

A Review on Functionally Graded Materials and Structures via Additive Manufacturing: From Multi‐Scale Design to Versatile Functional Properties

A Review on Tailoring Stiffness in Compliant Systems, via Removing Material: Cellular Materials and Topology Optimization

Studying Complex Evolution of Hyperelastic Materials under External Field Stimuli using Artificial Neural Networks with Spatiotemporal Features in a Small‐Scale Dataset

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