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Record W2282443712 · doi:10.4271/2004-01-0128

Energy Absorption Capacity for HPDC Components

2004· article· en· W2282443712 on OpenAlexaff
Cato Dørum, Odd Sture Hopperstad, Magnus Langseth, Odd-Geir Lademo, Stian Sannes

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2004
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsImpact
FundersNorges Forskningsråd
KeywordsEnergy (signal processing)Absorption (acoustics)Absorption capacityComputer scienceAutomotive engineeringMaterials sciencePhysicsEngineeringComposite materialChemical engineering

Abstract

fetched live from OpenAlex

The long-term objective of this work is to develop design and modeling tools that allow the structural behavior of thin-walled cast components to be predicted when subjected to static and dynamic loads such as in crash situations. Here, the energy absorption potential of High Pressure Die Cast components made of magnesium alloys AM20, AM50, AM60, AZ91 and the aluminum alloy AlSi7Mg is investigated using a shear-bolt principle. For the AM60 alloy, single plates cast with different thickness have been tested in order to investigate the effect of plate thickness on the shear-bolt mechanism. It is found that this deformation principle gives an approximately constant mean force during the deformation process. The behavior seems to be very robust, especially for the magnesium alloys. A simple empirical model for prediction of the mean shearing force as a function of plate thickness and bolt diameter is proposed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.004

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 head0.018
GPT teacher head0.214
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2004
Admission routes1
Has abstractyes

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