Improving Structural Design of Soft Actuators Using Finite Element Method Analysis

Ćurković, Petar; Jambrecic, Antonio

doi:10.7906/indecs.18.4.8

Cited by 8 publications

(7 citation statements)

References 29 publications

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“…Finite element modeling (FEM) is a commonly used numerical method. There have been multiple approaches ranging from using FEM in the design process [ 55 ], [ 58 ] to real-time control using asynchronous FEM [ 59 ] and reduced-order models [ 21 ],…”

Section: Soft Modelingmentioning

confidence: 99%

“…Using FEM for soft robots is challenging due to the multiple nonlinearities, including geometric, material, and contact [ 55 ]. A large focus of FEM in soft robotics is on material modeling using various hyperelastic models such as Neo-Hookean, Mooney-Rivlin, Yeoh, and Ogden [ 21 ], [ 58 ], [ 62 ]. Experimental testing is performed using material samples and the data is fit to these models to obtain the relevant parameters for use in the FEM softwares.…”

Section: Soft Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Characterization of Soft 3D Printed Actuators for Parallel Networks

Khetan

Blumenschein

2022

IEEE Robot. Autom. Lett.

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mm. The plots show the entire axial force versus pressure data obtained from the F/T transducer. The markers are only added for visibility and identification of the different actuator configurations. The legend markers signify the nominal ∆L and θ. . . . . . . . 3.8 Axial forces vs air inlet pressure for elongated actuator lengths. (a) Axial forces for different orientation angles when nominal ∆L = 1 mm, (b) Axial forces for different orientation angles when nominal ∆L = 2 mm. The plots show the entire axial force versus pressure data obtained from the F/T transducer. The markers are only added for visibility and identification of the different actuator configurations. The legend markers signify the nominal ∆L and θ. . . . . . . . 3.9 Shear forces vs air inlet pressure for compressed actuator lengths. (a) Shear forces for different orientation angles when nominal ∆L = −1 mm, (b) Shear forces for different orientation angles when nominal ∆L = −2 mm. The plots show the entire shear force versus pressure data obtained from the F/T transducer. The markers are only added for visibility and identification of the different actuator configurations. The legend markers signify the nominal ∆L and θ. . . . . . . . 3.10 Shear forces vs air inlet pressure for elongated actuator lengths. (a) Shear forces for different orientation angles when nominal ∆L = 1 mm, (b) Shear forces for different orientation angles when nominal ∆L = 2 mm. The plots show the entire shear force versus pressure data obtained from the F/T transducer. The markers are only added for visibility and identification of the different actuator configurations. The legend markers signify the nominal ∆L and θ. . . . . . . .

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Section: Soft Modelingmentioning

confidence: 99%

Section: Soft Modelingmentioning

confidence: 99%

Characterization of Soft 3D Printed Actuators for Parallel Networks

Khetan

Blumenschein

2022

IEEE Robot. Autom. Lett.

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“…[ 59–62 ] Using FEA and experimental methods, they found increasing surface area of pneumatic chambers and reducing their wall thickness increases bending. [ 28,63,64 ] However, chamber shape was not a significant determinant of bending as semicircular, square, and triangular chambers all produced similar pressure‐bending relationships. [ 65 ] Similarly, parametric optimizations of 3‐DOF (Z‐Tilt‐Tip) pneumatic actuators have maximized bending subject to ballooning and buckling constraints.…”

Section: Model‐based Design Optimizationmentioning

confidence: 99%

From Bioinspiration to Computer Generation: Developments in Autonomous Soft Robot Design

Pinskier

Howard

2021

Advanced Intelligent Systems

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The emerging field of soft robotics presents a new paradigm for robot design in which “precision through rigidity” is replaced by “cognition through compliance.” Lightweight and flexible, soft robots have vast potential to interact with fragile objects and navigate unstructured environments. Like octopuses and worms in nature, soft robots’ flexible bodies conform to hard objects and reconfigure for different tasks, delegating the burden of control from brain to body through embodied cognition. However, because of the lack of efficient modeling and simulation tools, soft robots are primarily designed by hand. Typically, hard components from rigid robots or living creatures are heuristically substituted for comparable soft ones. Autonomous design and manufacturing methodologies are urgently required to produce bespoke, high‐performing robots. Currently, design methodologies exist between simple but realistic parametric optimizations, and evolutionary algorithms which simulate morphology and control coevolution. To find high‐performing designs, novel high‐fidelity simulators and high‐throughput manufacturing and testing processes are required to explore the complex soft material, morphology and control landscape, blending simulation, and experimental data. This article reviews the state of the art in autonomous soft robotic design. Existing manual and automated designs are surveyed and future directions to automate soft robot design and manufacturing are presented.

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“…The term soft refers to materials with Young's modulus ~ 10 4 -10 9 Pa, which is comparable to biological tissues such as skin, muscles, and to a lesser extent bone. Traditional robots made of metal alloys have elastic moduli in the range ~ 10 9 -10 12 Pa. Engineering materials such as silicones, hydrogels, rubber, thermoplastics, fit well into the range of soft materials which makes them suitable for soft robotics applications [3]. The actuation of these soft structures can be achieved by various stimuli, including pressure of fluids, both pneumatics and hydraulics, electrical charges for electroactive polymers, chemical reactions, shape memory alloys, and magnetic effects [4,5].…”

Section: Introductionmentioning

confidence: 99%

Fused Deposition Modelling for 3D Printing of Soft Anthropomorphic Actuators

Ćurković¹,

Čubrić²

2021

Int. j. simul. model.

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Soft actuators are increasingly gaining attention in the robotics community due to several favourable properties rooted mainly in their inherent compliance. They are safe, can easily grasp different objects, and cheap to manufacture. However, particularly in the case of FDM (Fused Deposition Modelling) printed actuators, their design is usually an iterative process that relies on intuition due to complex material models, nonlinearities, large deformations, and residual stresses caused by imperfect interlayer structure. This makes such actuators difficult to model and control and limits their implementation despite some comparative advantages over the other 3D printing technologies. In this study, to characterize the properties of FDM printed soft actuators, we compare a simple but computationally effective linear model with a realistic experimentally generated hyperelastic material model of a soft actuator. Based on these insights, we 3D print a fully operational soft anthropomorphic hand and use it in a set of experiments to evaluate the limitations of the models and suggest design and printing parameters to improve soft actuators' performance.

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Improving Structural Design of Soft Actuators Using Finite Element Method Analysis

Cited by 8 publications

References 29 publications

Characterization of Soft 3D Printed Actuators for Parallel Networks

Characterization of Soft 3D Printed Actuators for Parallel Networks

From Bioinspiration to Computer Generation: Developments in Autonomous Soft Robot Design

Fused Deposition Modelling for 3D Printing of Soft Anthropomorphic Actuators

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