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Record W1978574231 · doi:10.4271/2015-01-1473

CAE Based Development of an Ejection Mitigation (FMVSS 226) SABIC using Design for Six Sigma (DFSS) Approach

2015· article· en· W1978574231 on OpenAlexaff
Kalu Uduma, D. Purushothaman, Darshan Subhash Pawargi, Sukhbir Bilkhu, Brian Beaudet

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2015
Typearticle
Languageen
FieldHealth Professions
TopicQuality and Safety in Healthcare
Canadian institutionsChrysler (Canada)
FundersNational Highway Traffic Safety AdministrationSaudi Basic Industries Corporation
KeywordsDesign for Six SigmaSix SigmaEngineeringComputer scienceManufacturing engineering

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">NHTSA issued the FMVSS 226 ruling in 2011. It established test procedures to evaluate countermeasures that can minimize the likelihood of a complete or partial ejection of vehicle occupants through the side windows during rollover or side impact events. One of the countermeasures that may be used for compliance of this safety ruling is the Side Airbag Inflatable Curtain (SABIC). This paper discusses how three key phases of the optimization strategy in the Design for Six Sigma (DFSS), namely, Identify; Optimize and Verify (I_OV), were implemented in CAE to develop an optimized concept SABIC with respect to the FMVSS 226 test requirements. The simulated SABIC is intended for a generic SUV and potentially also for a generic Truck type vehicle. The improved performance included: minimization of the test results variability and the optimization of the ejection mitigation performance of the SABIC.</div><div class="htmlview paragraph">Results from this study show that for generic SUV and Truck type vehicles with hardware similar to that tested in this study; <ul class="list disc"><li class="list-item"><div class="htmlview paragraph">Beltline overlap, loft size and pressure account for 75% of the variability in performance of an ejection mitigation SABIC. These metrics may therefore be considered critical to the design of the SABIC.</div></li><li class="list-item"><div class="htmlview paragraph">The identified key metrics may be used to define nominal ejection mitigation SABIC design best practice for the generic SUV and Truck.</div></li><li class="list-item"><div class="htmlview paragraph">An associated investigation during this study showed that a SABIC may shrink significantly when fully inflated. This shrinkage has the potential to move SABIC lofts away from the intended impact location.</div></li><li class="list-item"><div class="htmlview paragraph">The CAE based DFSS process has the potential to significantly reduce SABIC development time and may potentially enhance product speed to market using virtual tool versus empirically based SABIC development approach.</div></li></ul></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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
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 score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.002
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 head0.176
GPT teacher head0.414
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

Citations1
Published2015
Admission routes1
Has abstractyes

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