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Record W2058726560 · doi:10.1115/imece2007-41015

Redesign Auto Door Using Modularization and Ultra Light Steel Auto Body Structure

2007· article· en· W2058726560 on OpenAlexaff
I. Haider, S. A. Ali, Leo Oriet, J. A. Nooks

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsFord Motor Company (Canada)University of Windsor
Fundersnot available
KeywordsModular programmingAutomotive industryCost reductionManufacturing costManufacturing engineeringFuel efficiencyAutomotive engineeringProcess (computing)Reduction (mathematics)EngineeringComputer scienceMechanical engineeringBusiness

Abstract

fetched live from OpenAlex

This paper presents redesign of a car body for material weight reduction for the cost saving by material as well as fuel efficiency improvement. ULSAB (Ultra Light Steel Auto Body Structure) concept is used for weight reduction in redesigning process. Modularization is used for redesigning to reduce parts cost. Four factors: cost of modules, cost of assembly, common design and weight of the car are considered. Comparison for the material cost, weight and assembly time has been done for proposed design and existing design. Redesign car shows efficient fuel consumption car. Automotive sector could reduce their total manufacturing cost per vehicle by using modularization in design. The customers will get cheaper and fuel efficient car.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score0.450

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 head0.013
GPT teacher head0.249
Teacher spread0.235 · 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 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
Published2007
Admission routes1
Has abstractyes

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