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Shape Memory Alloy assisted healing of thermoplastic composite laminates under repeated impact loading

2025· article· en· W4407610976 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComposite Structures · 2025
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsConcordia University
Fundersnot available
KeywordsMaterials scienceComposite materialShape-memory alloyComposite numberThermoplasticAlloyThermoplastic compositesComposite laminates

Abstract

fetched live from OpenAlex

This study investigates the Low-Velocity Impact (LVI) performance and recovery capabilities of thermoplastic glass fiber composite laminates reinforced with Nitinol Shape Memory Alloy (SMA) wires. The potential of SMA-reinforced composite plates to absorb energy, restore the after-impact deformations and mitigate the properties degradation has been evaluated under repeated low-energy strikes and heat-treatment cycles. Repeated LVI tests were conducted at three distinct energy levels, with both hybrid and non-hybrid control sample groups subjected to impact and effective thermal recovery cycles. It was revealed that this newly presented hybridization method followed by thermal healing cycles enhanced the laminates’ resistance, significantly increasing the perforation threshold number of impacts. Moreover, the SMA-assisted recovery process effectively reduced permanent dent deformation, achieving over 50 % healing in specific iterations.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.157
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.283
Teacher spread0.269 · 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