Development of Image and Spectrum Data Driven Analysis for Soft Materials

AbstractWe present a novel, fully three-dimensional approach to soft material characterization and constitutive modeling with relevance to soft biological tissue. Our approach leverages recent advances in experimental techniques and data-driven computation. The experimental component of this approach involves in situ mechanical loading in a magnetic field (using MRI), yielding the entire deformation tensor field throughout the specimen regardless of the possible irregularities in its three-dimensional shape. Characterization can therefore be accomplished with data at a reduced number of deformation states. We refer to this experimental technique as MR-u. Its combination with powerful approaches to inverse modelling, specifically methods of model inference, would open the door to insightful mechanical characterization for soft materials. In recent computational advances that answer this need, we have developed new, data-driven inverse techniques to infer the model that best explains the physics governing observed phenomena from a spectrum of admissible ones, while maintaining parsimony of representation. This approach is referred to as Variational System Identification (VSI). In this communication, we apply the MR–u approach to characterize soft biological tissue and polymers, and using VSI, we infer the physically best-suited and parsimonious mathematical models of their mechanical response. We demonstrate the performance of our methods in the face of noisy data with physical constraints that challenge the identification of mathematical models, while attaining high accuracy in the predicted response of the inferred models.

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Joint Angle Estimation of a Tendon-Driven Soft Wearable Robot through a Tension and Stroke Measurement

Sensors ◽

10.3390/s20102852 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2852

Author(s):

Byungchul Kim ◽

Jiwon Ryu ◽

Kyu-Jin Cho

Keyword(s):

Joint Angle ◽

Index Finger ◽

Gaussian Process Regression ◽

Soft Materials ◽

Data Driven ◽

Wearable Robot ◽

Identification Methods ◽

Wearable Robots ◽

Different Shapes ◽

The Relationship

The size of a device and its adaptability to human properties are important factors in developing a wearable device. In wearable robot research, therefore, soft materials and tendon transmissions have been utilized to make robots compact and adaptable to the human body. However, when used for wearable robots, these methods sometimes cause uncertainties that originate from elongation of the soft material or from undefined human properties. In this research, to consider these uncertainties, we propose a data-driven method that identifies both kinematic and stiffness parameters using tension and wire stroke of the actuators. Through kinematic identification, a method is proposed to find the exact joint position as a function of the joint angle. Through stiffness identification, the relationship between the actuation force and the joint angle is obtained using Gaussian Process Regression (GPR). As a result, by applying the proposed method to a specific robot, the research outlined in this paper verifies how the proposed method can be used in wearable robot applications. This work examines a novel wearable robot named Exo-Index, which assists a human’s index finger through the use of three actuators. The proposed identification methods enable control of the wearable robot to result in appropriate postures for grasping objects of different shapes and sizes.

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Data-Driven GENERIC Modeling of Poroviscoelastic Materials

Entropy ◽

10.3390/e21121165 ◽

2019 ◽

Vol 21 (12) ◽

pp. 1165 ◽

Cited By ~ 4

Author(s):

Chady Ghnatios ◽

Iciar Alfaro ◽

David González ◽

Francisco Chinesta ◽

Elias Cueto

Keyword(s):

Indentation Depth ◽

Soft Materials ◽

Data Fitting ◽

Material Behavior ◽

Data Driven ◽

High Fidelity ◽

Force Microscopy ◽

Atomic Force ◽

Generic Modeling ◽

Generic Analysis

Biphasic soft materials are challenging to model by nature. Ongoing efforts are targeting their effective modeling and simulation. This work uses experimental atomic force nanoindentation of thick hydrogels to identify the indentation forces are a function of the indentation depth. Later on, the atomic force microscopy results are used in a GENERIC general equation for non-equilibrium reversible–irreversible coupling (GENERIC) formalism to identify the best model conserving basic thermodynamic laws. The data-driven GENERIC analysis identifies the material behavior with high fidelity for both data fitting and prediction.

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