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Record W2943087343 · doi:10.4271/2019-01-0339

iLok<sup>TM</sup> Nut - An Innovative Fastener that Solves a 30 Year Old Problem for Rear Axle Hub Assemblies

2019· article· en· W2943087343 on OpenAlexaff
Thales Sardinha Garcia Souza, Paulo César Sigoli, Kevin Weldi, Marcelo do Carmo Silva, Pierre Dionne

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2019
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsWeyerhauser (Canada)
Fundersnot available
KeywordsNutFastenerAxleComputer scienceEngineeringStructural engineeringMechanical engineering

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Truck and bus manufacturers have been constantly facing an issue to disassemble the rear axle shaft from the hub when transporting the truck from the factory to the dealership. In addition to that, the dealerships have the very same problem every time they have to replace the brake pads in some truck models, which leads to excessive service time, extra costs and aftermarket complaints. The current problematic fastening system is composed by a lock nut, a flat washer and a coned slotted bushing. The concept of this 30 year old design involves the coned slotted bushing being pressed against a tapered hole on the shaft’s flange. After tightening the lock nut, the bushing clamps towards the stud and it gets stuck in between the shaft and the stud generating the problem described above. This paper shows the R&D process that Tekfor used to come up with the 1-piece fastener named iLok<sup>TM</sup> nut that replaces the problematic 3-piece fastening system. The methodology involved (a) Innovation process; (b) CAD modeling; (c) Finite Element Analysis; (d) Forging simulations; (e) Forging trials; (f) Prototypes manufacturing; (g) Torque and tension tests; (h) Junker vibration tests; (i) Proof load test; (j) Salt spray test; (k) Fit and function test, etc. The 1-piece fastener showed superior performance in all these tests and its usage brings multiple benefits to OEMs, Tiers 1 suppliers, Dealerships and end users. This is the most important innovation of a nut for the automotive industry since the advent of the wheel nut in 1972.</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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.257
Teacher spread0.238 · 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.

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
Published2019
Admission routes1
Has abstractyes

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