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Record W4390746557 · doi:10.1080/15376494.2024.2301923

Modular assembly for multicell structures with designable energy absorption characteristics

2024· article· en· W4390746557 on OpenAlexaff
Kui Wang, Huijing Gao, Jin Wang, Depeng Wang, Wei Wen, Yong Peng, Tianjian Yu

Bibliographic record

VenueMechanics of Advanced Materials and Structures · 2024
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsMinistry of Education and Child Care
FundersFundamental Research Funds for Central Universities of the Central South University
KeywordsModular designMaterials scienceComposite materialThermoplasticPolyamideAbsorption (acoustics)Structural engineeringMechanical engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

In this study, a novel modular multicell structure was proposed to achieve tunable energy absorption characteristics. Dovetail grooves and protrusions were designed on square thin-walled tubes as interfaces to realize the modular assembly. The quasi-static compression experimental results showed that single thin-walled tubes made of thermoplastic polyurethanes and short carbon-fiber-reinforced polyamide exhibited high elasticity and high compressive strength, respectively. Due to the different mechanical properties of the two materials, energy absorption performance of modular structures reached different levels with the variation of cell materials. The concept of modular multicell structure provided possibility to customize energy absorbers with desired properties.

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)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.194
Teacher spread0.190 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

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