Prediction of fatigue life of a flexible foldable origami antenna with Kresling pattern

Moshtaghzadeh, Mojtaba; Izadpanahi, Ehsan; Mardanpour, Pezhman

doi:10.1016/j.engstruct.2021.113399

Cited by 27 publications

(10 citation statements)

References 25 publications

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“…This, together with the position of the modified panel in the origami module and their direction of rotation, constitute the parameters of a rich design space that we can efficiently scan with a custom greedy algorithm. While in this study we have used a simple geometric model to identify optimal designs, a fully mechanical model [65,66] that accounts for the effect of gravity, the pressure drop during the snap-through transition as well as the non-rigid coupling between the units would reduce the error between numerical predictions and experimental results. In addition, the current design space could be further expanded through investigating the effect of other geometrical parameters (e.g., l, h, and α) on the resulting deformation of the modules, as well as expanding the range of the considered values of Δ.…”

Section: Discussionmentioning

confidence: 99%

Inflatable Origami: Multimodal Deformation via Multistability

Melancon

Forte

Kamp

et al. 2022

Adv Funct Materials

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Inflatable structures have become essential components in the design of soft robots and deployable systems as they enable dramatic shape change from a single pressure inlet. This simplicity, however, often brings a strict limitation: unimodal deformation upon inflation. Here, multistability is embraced to design modular, inflatable structures that can switch between distinct deformation modes as a response to a single input signal. This system comprises bistable origami modules in which pressure is used to trigger a snap-through transition between a state of deformation characterized by simple deployment to a state characterized by bending deformation. By assembling different modules and tuning their geometry to cause snapping at different pressure thresholds, structures capable of complex deformations that can be preprogrammed and activated using only one pressure source are created. This approach puts forward multistability as a paradigm to eliminate a one-to-one relation between input signal and deformation mode in inflatable systems.

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Section: Discussionmentioning

confidence: 99%

Inflatable Origami: Multimodal Deformation via Multistability

Melancon

Forte

Kamp

et al. 2022

Adv Funct Materials

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“…The Kresling mode is similar to those mountains and valley creases which are alternately tilted in the directions of torsions [16,17]. This design principle is suitable for those applications which require uniaxial expansion, such as damping springs [18,19], telescopic booms [20][21][22] and crawler robots [23][24][25]. Although there are a lot of studies on quasistatic mechanics [26,27], dynamics [28,29], or low-amplitude wave response [30] for these Kresling origami structures, a critical aspect has been overlooked because these Kresling structures were always treated in a hexagonal or even sided polygon model.…”

Section: Introductionmentioning

confidence: 99%

Multimodal origami shape memory metamaterials undergoing compression–twist coupling

Hua

Shu

et al. 2023

Smart Mater. Struct.

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As origami structures display designable and predictable folding or unfolding shape changes, the origami-inspired mechanical metamaterials have recently been extensively investigated for applications in metamaterial engineering. There were many previous studies on the conventional hexagonal Kresling origami structures, however, there are many issues such as structural optimizations and designable strategies for the mechanical metamaterials, which have not been solved. To solve these issues, in this study, we studied the influences of crease direction, the number of sides, and unit arrangement on the origami structures. Effects of these parameters on mechanical properties and deformation behaviors of metamaterials were analyzed using finite element methods and experimental verifications. By adjusting the number of sides, the switching between monostability and bistability of the metamaterials was realized. The compression-twist coupling effect of these metamaterials can be adjustable and tailorable by arranging the chosen units in series. Designed foldable metamaterials are flexible, especially in their unfolding and folding directions, resulting in the achievement of an unstable compression state, i.e., the externally applied loads may cause the structure to unfold along the same compression path. Furthermore, shape memory polymer (SMP) has been printed using 3D printing technology to achieve the smart origami metamaterials, which endow the metamaterials with shape memory effect, self-adaptability and temperature-responsive mechanical behavior.

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“…Data-driven surrogate modeling has proved its usage in the design procedure of highfrequency devices as a low-cost surrogate of the various electrical and field responses of high-frequency stages such as scattering parameters [S], 14,15 reflection phase characteristics in reflect-arrays, 16 characteristic impedance, 17 and prediction resonant frequency of antenna designs. [18][19][20] In each of the mentioned works, different types of Artificial Intelligence (AI) regression methods such as polynomial, 21,22 kriging, [23][24][25] Support Vector Regression (SVR), [26][27][28][29] Artificial Neural Networks (ANNs), [30][31][32][33][34] and Deep Learning (DL) [35][36][37][38][39] had been used to create an accurate, stable mapping between the given input space of the problem and the targeted characteristic as the output of the model.…”

Section: Introductionmentioning

confidence: 99%

Data driven surrogate modeling of horn antennas for optimal determination of radiation pattern and size using deep learning

Piltan

Kīzīlay

Belen

et al. 2023

Micro & Optical Tech Letters

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Horn antenna designs are favored in many applications where ultra‐wide‐band operation range alongside of a high‐performance radiation pattern characteristics are requested. Scattering‐parameter characteristics of antennas is an important design metric, where inefficiency in the input would drastically lower the realized gain. However, satisfying the requirement for scattering parameters are not enough for having an antenna with high‐performance results, where the radiation characteristic of the design can be changed independently than the scattering parameters behavior. A design might have a high‐efficiency performance, but the radiation characteristics might not be acceptable. Furthermore, there are other design considerations such as size and volume of the design alongside of these conflicting characteristics, which directly affect the manufacturing cost and limits the possible applications. In this work, by using data‐driven surrogate modeling, it is aimed to achieve a computationally efficient design optimization process for horn antennas with high radiation performance alongside of being small in or within the limits of the desired application limits. Here, the geometrical design variables, operation frequency, and radiation direction of the design will be taken as the input, while the realized gain of the design is taken as the output of the surrogate model. Series of powerful and commonly used artificial intelligence algorithms, including Deep Learning had been used to create a data‐driven surrogate model representation for the handled problem, and 80% computational cost reduction had been obtained via proposed approach. As for the verification of the studied optimization problem, an optimally designed antenna is prototyped via the use of three‐dimensional printer and the experimental results ware compared with the results of surrogate model.

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Prediction of fatigue life of a flexible foldable origami antenna with Kresling pattern

Cited by 27 publications

References 25 publications

Inflatable Origami: Multimodal Deformation via Multistability

Inflatable Origami: Multimodal Deformation via Multistability

Multimodal origami shape memory metamaterials undergoing compression–twist coupling

Data driven surrogate modeling of horn antennas for optimal determination of radiation pattern and size using deep learning

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