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Three-dimensional modeling of hard-magnetic soft continuum robots with composite magnetoactive elastomers under nonuniform magnetic fields

2025· article· en· W4415979861 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueComposites Part B Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsConcordia University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsMagnetic fieldNonlinear systemFinite element methodMagnetTorqueBackward Euler methodCoupling (piping)BifurcationEuler's formulaGalerkin method

Abstract

fetched live from OpenAlex

This study presents a novel theoretical and experimental investigation through the development of a comprehensive three-dimensional analytical framework for hard-magnetic soft continuum robots (HMSCRs) actuated by nonuniform magnetic fields, explicitly incorporating the magnetic field gradient generated by a permanent magnet through both magnetic torque and body force, while also accounting for axial strain and gravity. The permanent magnet’s five degrees of freedom, including three translational and two rotational motions, are embedded in the formulation to capture realistic field–structure coupling for arbitrary poses. The geometrically nonlinear behavior of the HMSCR, involving coupled stretching, twisting, and nonplanar bending, is represented using Euler angles. To address Euler singularities, an adaptive switching mechanism is designed to automatically switch between the ZYX and YZX Euler sequences, effectively mitigating gimbal lock. The model is derived from the principle of minimum potential energy and solved using the Galerkin method with a dogleg optimization algorithm. A deep neural network surrogate, trained on finite element magnetic field data and fine-tuned with experimental measurements, enables rapid prediction of nonuniform magnetic fields. A novel experimental setup is developed, featuring a precision-molded HMSCR actuated by a six-degree-of-freedom robotic arm that positions and orients the magnet within a calibrated workspace. The proposed model is validated through benchmark studies, including comparative analyses with quaternion-based formulations and new experiments, all demonstrating excellent agreement between the developed model and experimental and numerical results. Moreover, numerical analyses, including bifurcation analysis, are conducted to assess the three-dimensional nonlinear response of the HMSCR under realistic nonuniform magnetic fields.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.642
Threshold uncertainty score1.000

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 head0.008
GPT teacher head0.193
Teacher spread0.185 · 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