MétaCan
Menu
Back to cohort
Record W4287854319 · doi:10.1039/d2sm00638c

How friction and adhesion affect the mechanics of deep penetration in soft solids

2022· article· en· W4287854319 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

VenueSoft Matter · 2022
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaHuman Frontier Science Program
KeywordsPenetration (warfare)Materials scienceSoft materialsAdhesionMechanicsRADIUSPenetration depthComposite materialClassical mechanicsNanotechnologyPhysicsOpticsEngineeringComputer science

Abstract

fetched live from OpenAlex

The mechanics of puncture and soft solid penetration is commonly explored with the assumption of frictionless contact between the needle (penetrator) and the specimen. This leads to the hypothesis of a constant penetration force. Experimental observations, however, report a linear increment of penetration force with needle tip depth. This force increment is due to friction and adhesion, and this paper provides its correlation with the properties of the cut material. Specifically, the force-depth slope depends on the rigidity and toughness of the soft material, the radius of the penetrator and the interfacial properties (friction and adhesion) between the two. We observe that adhesion prevails at relatively low toughness, while friction is dominant at high toughness. Finally, we compare our results with experiments and observe good agreement. Our model provides a valuable tool to predict the evolution of penetration force with depth and to measure the friction and adhesion characteristics at the needle-specimen interface from puncture experiments.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.174

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 head0.201
Teacher spread0.193 · 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