FEA-Based Inverse Kinematic Control: Hyperelastic Material Characterization of Self-Healing Soft Robots

Ferrentino, Pasquale; Tabrizian, Seyedreza Kashef; Brancart, Joost; Assche, Guy Van; Vanderborght, Bram; Terryn, Seppe

doi:10.1109/mra.2021.3132803

Cited by 14 publications

(11 citation statements)

References 21 publications

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“…Material models that are fitted onto these mechanical behaviors can be adopted in FEA simulations used to design or control (robotic) components. An example is presented by Ferrentino et al , in which constitutive material laws are fitted onto the hyperelastic behavior of a DPBM-FT5000- r 0.5 network. This fitted neo-Hookean law is used to simulate and control a healable soft robotic actuator made from the DA network.…”

Section: Discussionmentioning

confidence: 99%

Structure–Property Relationships of Self-Healing Polymer Networks Based on Reversible Diels–Alder Chemistry

et al. 2022

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Recently, the Diels–Alder reaction between furan and maleimide functional groups has been exploited in dynamic reversible covalent networks, introducing a healing ability and reprocessing in various applications. Both applications and (re)processing techniques impose specific requirements, which can be met by the large tunability of the material properties of Diels–Alder-based networks. The extensive set of structure–property relations presented in this paper allows for designing Diels–Alder networks meeting a broad range of material properties. Three major network design parameters are defined that influence these properties: the Diels–Alder concentration, the degree of functionality of the reactive maleimide and furan groups on the monomers, and the stoichiometric ratio between these reactive groups. Their individual influences on the kinetics and the thermomechanical, rheological, and healing properties were investigated via tensile testing, differential scanning calorimetry, dynamic mechanical analysis, and dynamic rheometry. By tuning the design parameters independently, mechanical properties can be matched with requirements imposed by applications by changing the Diels–Alder concentration, while healing speeds can be further altered by the stoichiometric ratio between maleimide and furan groups. In addition, the temperature of gelation, crucial for processing, can be tuned by means of the functionality parameter, meeting requirements imposed by manufacturing techniques.

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Section: Discussionmentioning

confidence: 99%

Structure–Property Relationships of Self-Healing Polymer Networks Based on Reversible Diels–Alder Chemistry

et al. 2022

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“…The geometrical dimensions of this actuator can be found in Fig. 3, and are similar to the design in a previous publication [18]. The geometric structure parameters are fixed, and outof-plane deformation is addressed solely by changing the material distribution in the actuator.…”

Section: B Topology Optimizationmentioning

confidence: 99%

“…The Moonley-Rivlin model, composed of five parameters, was fitted onto experimental stress-strain data derived via tensile testing of both materials (Table I). This fitting was performed in Abaqus using the Levenberg-Marquardt method [18] and can be seen in Fig. 2.…”

Section: Modeling and Optimization A Fitting Constitutive Materials M...mentioning

confidence: 99%

“…The optimized design is manufactured to validate the simulations and is bench marked against a reference actuator, composed of a single hyperelastic material. This is done by analyzing the in-plane and out-of plane deformation of the actuators in a dedicated experimental setup [18]. Next, the validated simulation and the optimized material distribution are used to further study the influence of the ratio between the Young's modulus of the two materials on the out-of-plane deformation resistance.…”

Section: Introductionmentioning

confidence: 99%

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Topology optimized multi-material self-healing actuator with reduced out of plane deformation

Wang

Terryn

Legrand

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…To calibrate the sensor, and get a relation between the bending angle of the finger and the resistance of the sensor, a camera can be used [ 27 ]. The bending angle is defined as the angle between the resting position of the finger and the line connecting base and tip of the finger.…”

Section: Tutorial Descriptionmentioning

confidence: 99%

An Interdisciplinary Tutorial: A Self-Healing Soft Finger with Embedded Sensor

Roels

Terryn

Ferrentino

et al. 2023

Sensors

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In the field of soft robotics, knowledge of material science is becoming more and more important. However, many researchers have a background in only one of both domains. To aid the understanding of the other domain, this tutorial describes the complete process from polymer synthesis over fabrication to testing of a soft finger. Enough background is provided during the tutorial such that researchers from both fields can understand and sharpen their knowledge. Self-healing polymers are used in this tutorial, showing that these polymers that were once a specialty, have become accessible for broader use. The use of self-healing polymers allows soft robots to recover from fatal damage, as shown in this tutorial, which increases their lifespan significantly.

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FEA-Based Inverse Kinematic Control: Hyperelastic Material Characterization of Self-Healing Soft Robots

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Cited by 14 publications

References 21 publications

Structure–Property Relationships of Self-Healing Polymer Networks Based on Reversible Diels–Alder Chemistry

Structure–Property Relationships of Self-Healing Polymer Networks Based on Reversible Diels–Alder Chemistry

Topology optimized multi-material self-healing actuator with reduced out of plane deformation

An Interdisciplinary Tutorial: A Self-Healing Soft Finger with Embedded Sensor

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