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Record W3100206791 · doi:10.1177/1464420720972431

Experimental, numerical, and multi-objective optimization investigations on the energy absorption features of single- and bi-layer deep-drawn cups

2020· article· en· W3100206791 on OpenAlexafffund
M. A. Ghasemabadian, Mehran� Kadkhodayan, William Altenhof

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part L Journal of Materials Design and Applications · 2020
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceAxial symmetryAbsorption (acoustics)AluminiumComposite materialWeldingLayer (electronics)Tube (container)Total energyStructural engineeringDisplacement (psychology)Engineering

Abstract

fetched live from OpenAlex

In this article, the energy absorption features of single- and bi-layer deep-drawn cups (S- and B-cups, respectively) under a quasi-static axial loading are investigated experimentally and numerically. The S-cups were made of 304L stainless steel and explosively welded B-cups were composed of aluminum and 304L stainless steel layers. A multi-objective optimization was performed on specific energy absorption and initial peak force based on the polynomial response surface method. Furthermore, to compare the energy absorption features of deep-drawn cups, two groups of 304L stainless steel tubes (with the same mass or the same height as the S-cups) were axially compressed. The experimental results indicated that the S-cups experienced total energy absorption and mean crush force approximately 24% and 51% greater than those of tubes with the same mass and thickness, respectively. Furthermore, the total efficiency and specific total efficiency of the S-cups were approximately 0.23 and 1.82 times greater than those of tubes with the same height and thickness. Moreover, the energy absorbing effectiveness factor of B-cups was approximately twice of the S-cup.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.645
Threshold uncertainty score0.282

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 head0.022
GPT teacher head0.214
Teacher spread0.191 · 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

Citations6
Published2020
Admission routes2
Has abstractyes

Explore more

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