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Record W2460402063 · doi:10.1115/1.4033967

Manufacture and Mechanics of Topologically Interlocked Material Assemblies

2016· article· en· W2460402063 on OpenAlexafffund
Thomas Siegmund, François Barthelat, Raymond J. Cipra, Ed Habtour, Jaret C. Riddick

Bibliographic record

VenueApplied Mechanics Reviews · 2016
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsMcGill University
FundersAir Force Office of Scientific ResearchNatural Sciences and Engineering Research Council of CanadaU.S. Army
KeywordsAdaptabilityScaffoldMaterials scienceMechanical engineeringComputer scienceProperty (philosophy)NanotechnologyEngineering

Abstract

fetched live from OpenAlex

Topologically interlocked material (TIM) systems are load-carrying assemblies of unit elements interacting by contact and friction. TIM assemblies have emerged as a class of architectured materials with mechanical properties not ordinarily found in monolithic solids. These properties include, but are not limited to, high damage tolerance, damage confinement, adaptability, and multifunctionality. The review paper provides an overview of recent research findings on TIM manufacturing and TIM mechanics. We review several manufacturing approaches. Assembly manufacturing processes employ the concept of scaffold as a unifying theme. Scaffolds are understood as auxiliary support structures employed in the manufacturing of TIM systems. It is demonstrated that the scaffold can take multiple forms. Alternatively, processes of segmentation are discussed and demonstrated. The review on mechanical property characteristics links the manufacturing approaches to several relevant material configurations and details recent findings on quasi-static and impact loading, and on multifunctional response.

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 score0.004

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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

Citations74
Published2016
Admission routes2
Has abstractyes

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