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Record W4403263318 · doi:10.1002/srin.202400308

Effect of Pressure on the Linear Friction Welding of a Tool Steel and a Low‐Alloy Carbon Steel

2024· article· en· W4403263318 on OpenAlexafffund
O.A. Zambrano, Javad Gholipour, Priti Wanjara, Jiaren Jiang

Bibliographic record

Venuesteel research international · 2024
Typearticle
Languageen
FieldMaterials Science
TopicMetal Alloys Wear and Properties
Canadian institutionsNational Research Council Canada
FundersNational Research Council Canada
KeywordsMaterials scienceMetallurgyCarbon steelAlloyWeldingAlloy steelFriction weldingCarbon fibersComposite materialComposite numberCorrosion

Abstract

fetched live from OpenAlex

This study investigates the effect of pressure (burn‐off and forging) on the mechanical properties of the joint between a wear‐resistant tool steel and a low‐alloy steel using linear friction welding. The authors have previously demonstrated the feasibility of joining these dissimilar materials, but the impact of pressure on the mechanical properties of the bimaterial joint remains unclear. To address this, weld samples are prepared using different pressures and are characterized through microstructural analysis, microhardness, tensile testing, and fractography. The results show that the strength of the joint between the wear‐resistant tool steel and the low‐alloy carbon steel increases as the pressure increases up to a certain point, after which a decrease is observed. The highest joint strength is achieved at a pressure of 360 MPa. The microhardness profile measurement reveals a distinct transition zone at the interface between the two materials, with varying hardness values. The hardness of the low‐alloy carbon steel increases near the interface, while that of the wear‐resistant tool steel decreases. This transition zone is found to be narrower at higher pressures. Microstructural characterization shows that the grain structure near the interface differs from that of the starting base materials.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.029
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 head0.049
GPT teacher head0.352
Teacher spread0.303 · 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

Citations11
Published2024
Admission routes2
Has abstractyes

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