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Record W6980050109

Artificial muscle: materials and actuation systems for mimicking the mechanical properties of biological muscle

2011· article· en· W6980050109 on OpenAlexaboutno aff

Bibliographic record

VenueElectronic Theses and Dissertations Repository (University of Pisa) · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsnot available
Fundersnot available
KeywordsActuatorVariety (cybernetics)Artificial muscleBiological materialsMechanical impedanceWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

Many years have been spent to study different kinds of actuators and still researches are in progress to design more powerful and compact systems able to generate force. Proofs of concept were realized using a wide variety of materials and structures, and some of them re-sulted to be outstanding actuators for their performances at the time they were built. In the se-cond half of the 20th century, actuators studies were focused to build prototypes that could have only better strictly mechanical features since it wasn’t put attention to humanoid robot¬ics and mimicking of muscle’s properties. However, in the last decades, actuators were also de-fined “artificial muscles”, including in the definition active materials able to produce a force and deform themselves through different energy sources, as well as concepts that aim to have muscle-like properties and behave as a real biological muscle. What emerges from literature on this topic is the difficulty to overcome issues such as large size, complex structures, high mechanical impedance while at the same time obtaining mechanical characteristics comparable to muscles and, as a more challenging feature, repro-duce muscle’s active and passive behavior. The present master thesis work is part of the development of new technologies and de-sign principles for the conception of actuators capable of replicating the active and passive behavior of muscle. A paper published on Nature journal by Lv et al. in 2010 on a novel titin-mimetic material inspired the work presented in this thesis, developed in collaboration with Prof. Li, leader of the team, and Lv (University of British Columbia, Canada). The aim of this thesis is the analysis and modeling of mechanical passive properties of the titin-like material in order to evaluate the possibility of employing it in design and devel-opment of biomimetic actuator for robotics, bioengineering and prosthetic applications. The first specific objective of this work is the study of the best suitable materials that state of the art on artificial muscle reports in the mimicking of active and passive behavior and muscle’s macroscopic properties. The second objective of thesis is the development of a lumped-parameter non-linear viscoelastic mechanical model of the titin-like material able to simulate its passive properties for the designing of an muscle-like actuator. The third purpose is the design and building of custom mechanical components suita-ble to perform a mechanical characterization on the titin-like material, provided by Lv and colleagues. Fourth objective is the performance of a mechanical characterization on a titin-like material sample through stress-relaxation and stress-strain tests at different strain-levels and stretch-speeds, in order to validate the previously designed model. The present work can be considered the preliminary stage of a more complex pathway towards a better knowledge of the material behavior, the ability to produce it in different shapes and sizes and the modalities of embedding it into a novel artificial muscle. An exten-sive study, in collaboration with the Canadian research team that designed and produced sam-ples of titin-like material, will be conducted to investigate the possible exploitation of a suita-ble active material that, working together with the titin-like material, could be able to confer a muscle-like behavior to a novel kind of artificial muscle.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.263

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.202
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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