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Record W4409017311 · doi:10.4271/2025-01-8327

Mechanical Analysis of a Non-Pneumatic Tire’s Spokes

2025· article· en· W4409017311 on OpenAlexaff
William Collings, Chengzhi Li, Jackson A. Schwarz, Akhlesh Lakhtakia, Charles E. Bakis, Zeinab El-Sayegh, Moustafa El–Gindy

Bibliographic record

VenueSAE International Journal of Advances and Current Practices in Mobility · 2025
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsAutomotive engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Mechanical analysis was performed of a non-pneumatic tire, specifically a Michelin Tweel size 18x8.5N10, that can be used up to a speed of 40 km/h. A Parylene-C coating was added to the rubber spoke specimens before performing both microscopic imaging and cyclic tensile testing. Initially, standard ASTM D412 specimens type C and A were cut from the wheel spokes, and then the specimens were subjected to deposition of a nanomaterial. The surfaces of the specimens were prepared in different ways to examine the influence on the material behavior including the stiffness and hysteresis. Microscopic imaging was performed to qualitatively compare the surfaces of the coated and uncoated specimens. Both coated and uncoated spoke specimens of each standard type were then subjected to low-rate cyclic tensile tests up to 500% strain. The results showed that the Parylene-C coating did not affect the maximum stress in the specimens, but did increase the residual strain. Type C specimens also had a higher maximum stress on average than the Type A specimens. These mechanical tests provide useful data to determine material properties, such as Ogden material parameters, for future simulation of both the hyperelastic and hysteresis behavior of the rubber spokes.</div></div>

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)0.001
metaresearch head score (Gemma)0.001
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 score0.648
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.021
GPT teacher head0.393
Teacher spread0.372 · 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 designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

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