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Record W4386811656 · doi:10.1088/1361-665x/acfa7c

Focus on 4D materials design and additive manufacturing

2023· article· en· W4386811656 on OpenAlexaff
Mahdi Bodaghi, Suong V. Hoa, Thomas Gries, Antoine Le Duigou, Yonas Tadesse, Lining Yao, Ali Zolfagharian

Bibliographic record

VenueSmart Materials and Structures · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsConcordia University
Fundersnot available
KeywordsAerospace3D printingAutomotive industryFocus (optics)Field (mathematics)3d printedAdaptabilityRoboticsManufacturing engineeringEngineeringComputer scienceSystems engineeringMechanical engineeringNanotechnologyRobotArtificial intelligenceAerospace engineeringMaterials scienceManagement

Abstract

fetched live from OpenAlex

Four-dimensional (4D) printing involves the creation of materials that can change their shape, structure, or functionality over time in response to external stimuli. This incredible adaptability opens new possibilities in fields ranging from robotics to biomedical applications. This focus issue in Smart Materials and Structures is dedicated to the latest research in the dynamic field of 4D materials design and additive manufacturing. With a rapidly evolving landscape, we aimed to explore various exciting topics that are at the forefront of scientific advancements. This focus issue shed light on the current industrial applications of 4D printing. In fields such as medical, aerospace, automotive, defense, and mechanical engineering, 4D printing has already made significant strides. From customized implants and prosthetics in healthcare to adaptive structures and components in aerospace and automotive industries, the practical applications of 4D printing are diverse and impactful. This focus issue includes nine original research articles jointly made by involvement of more than 30 active academics in the field from diverse international centres and universities.

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: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.857

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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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