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Record W3096497355 · doi:10.3390/met10111425

Fabrication of Magnesium–NiTip Composites via Friction Stir Processing: Effect of Tool Profile

2020· article· en· W3096497355 on OpenAlex
Namrata Gangil, Harsh Nagar, Sohail M.A.K. Mohammed, D. Singh, Arshad Noor Siddiquee, Sachin Maheshwari, D.L. Chen

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

VenueMetals · 2020
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFriction stir processingMaterials scienceFabricationComposite materialMicrostructureMagnesiumBrittlenessHomogeneity (statistics)Indentation hardnessMetallurgy

Abstract

fetched live from OpenAlex

In this study, a solid-state fabrication route via friction stir processing (FSP) was used to fabricate Nitinol particulate (NiTip)-reinforced magnesium-based composites to avoid the diffusion reaction and the formation of brittle interfacial compounds. The effect of four tool profiles on the homogeneity in the dispersion of NiTip particles in the magnesium matrix and microhardness was examined and analyzed. A counter-clockwise scrolled shoulder with a plain cylindrical pin and three tools with a flat shoulder having plain cylindrical pin, left-hand, and right-hand threaded pins were used and compared. The tool profiles were observed to exhibit a significant influence on the microstructure of the fabricated Mg/NiTip composites. A wider and more uniform distribution of NiTip particles along with superior bonding with magnesium matrix was achieved with a left-hand threaded cylindrical pin tool. The incorporation of NiTip gave rise to a significant increase in the microhardness of the fabricated composites due to a variety of strengthening mechanisms.

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 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 score0.174
Threshold uncertainty score0.512

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 head0.207
Teacher spread0.195 · 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